Socio-Economic Burden of Disease: The COVID-19 Case

Tomé, Eduardo (Ed.); Garavan, Thomas (Ed.); Dias, Ana (Ed.) Book Socio-Economic Burden of Disease: The COVID-19 Case Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Tomé, Eduardo (Ed.); Garavan, Thomas (Ed.); Dias, Ana (Ed.) (2024) : SocioEconomic Burden of Disease: The COVID-19 Case, ISBN 978-3-7258-0290-6, MDPI - Multidisciplinary Digital Publishing Institute, Basel, https://doi.org/10.3390/books978-3-7258-0290-6 This Version is available at: https://hdl.handle.net/10419/305342 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ mdpi.com/journal/healthcare Special Issue Reprint Socio-Economic Burden of Disease The COVID-19 Case Edited by Eduardo Tomé, Thomas Garavan and Ana Dias Socio-Economic Burden of Disease: The COVID-19 Case Socio-Economic Burden of Disease: The COVID-19 Case Editors Eduardo Tom´e Thomas Garavan Ana Dias Basel •Beijing •Wuhan •Barcelona •Belgrade •Novi Sad •Cluj •Manchester Editors Eduardo Tom´ e Universidade Lus´ ofona Lisboa Portugal Thomas Garavan University College Cork Cork Ireland Ana Dias Universidade de Aveiro Aveiro Portugal Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Healthcare (ISSN 2227-9032) (available at: https://www.mdpi.com/journal/healthcare/special issues/socio-economic burden of disease covid-19 case). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: Lastname, A.A.; Lastname, B.B. Article Title. Journal Name Year,Volume Number, Page Range. ISBN 978-3-7258-0289-0 (Hbk) ISBN 978-3-7258-0290-6 (PDF) doi.org/10.3390/books978-3-7258-0290-6 © 2024 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license. About the Editors Eduardo Tom´e Eduardo Tom´ e B Econ, M Econ, PhD, is an active researcher and teacher in the domain of the economics of human resources. He also plays an important role in the network of research among active European academics in his field of study. To date, he has authored or co-authored 64 papers published in peer-reviewed journals and 115 papers in peer-reviewed conference proceedings. He has also authored 15 book chapters. He is also a member of the Editorial Board of several peer-reviewed journals. He has received several Honours, including two Prizes for Scientific Achievement. Eduardo Tome teaches a variety of subjects on both undergraduate and post-graduate programmes. He is also active as a supervisor, working with undergraduates, master’s students and PhD students. He is noted for his success with both master’s and doctoral students. He also lectures on a PhD programme, and is equally comfortable teaching in both Portuguese and English. He can also speak, read and write in French and Spanish. Long before COVID-19, he used online platforms (emails, Moodle, Blackboard, Skype, etc) as a complementary form of communication with students and colleagues, a situation that has been made essential after the pandemic. Eduardo Tome has a significant presence among the academic community in general, and especially among those interested in intangibles. Since 2009, he has organized well-attended international conferences for his university on themes such as knowledge management, human resource development, and intellectual capital. With friends, he created the TAKEConference, which has been held every year since 2016, with the exception of 2020. These conferences have augmented the external focus and international relationships of his activities. All conferences have resulted in spin-offs such as Special Issues in journals. These events have brought both prestige and financial rewards to the university. Currently, he is a member of the GOVCOPP of Universidade de Aveiro, Intrepid of UTAD and teaches at Universidade Lusofona. Thomas Garavan Thomas Garavan is Professor of Leadership Practice in CUBS, UCC. He was recently listed in the Stanford University Science-Wide author citation indicators 2020 as one of the top 2% of academics in Economics and Business. He is a world leading expert in leadership development, learning and development and HRD. He has published 185 journal articles, 16 books, 26 book chapters and 6 monographs. He has published extensively in leading HRD journals including HRDQ, HRDR, ADHR and HRDI. He has also published extensively in the top four HRM journals: HRM (US) HRMJ, Personnel Review and IJHRM. In addition, he has published extensively in management journals including the International Journal of Management Reviews, European Management Review, Journal of Business Research, Tourism Management, Information Technology and People, International Small Business Journal, Thunderbird International Review and the Journal of Sleep Research and Business Ethics: A European Review. His most recent book publications include Learning and Development in Organizations: A Systems-Informed Model of Effectiveness (Palgrave), Strategic Human Resource Management (Oxford University Press), Handbook of International Human Resource Development (Edward Elgar) and Global Human Resource Development (Routledge). He is co-editor of the European Journal of Training and Development and Associate Editor of Personnel Review and is a member of the HRDQ, HRDI, HRDR, ADHR, HRMJ, International Journal of Training and Development and International Journal of Human Resource Management. He has extensive teaching experience with undergraduate, post-graduate and post-experience students, in addition to executive education and leadership development. He was recently elected to the Hall of Fame of the Academy of Human vii Resource Development, USA, and has won numerous awards for publication and journal editing. Ana Dias Ana Alexandra da Costa Dias has a PhD in Health Sciences and Technologies from the University of Aveiro (2015), a Masters in Innovation and Knowledge Management from the University of Aveiro (2005) and a degree in Management from the Institute of Superior, Financial and Fiscal Studies (1998). She has taught at the University of Aveiro since 2005 and is currently an Assistant Professor in the Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT) at UA. She has been teaching the following subjects: Models and Business Processes, Organizational Behaviour and Organizational and Social Health Structures. Her areas of interest focus on organizational models of health care provision and workflow management with applications in the health care sector and health policy. She has participated in some UA research projects in collaboration with the social sector and the health sector, and she is author and co-author of scientific articles published in national and international journals and has several publications in national and international conference proceedings. She also cooperates as a researcher with the research unit on Governance, Competitiveness and Public Policies (GOVCOPP). viii Preface In 2020, the world was shaken by a very unexpected development, an unseen virus which could kill millions and spread without control. To reduce the impact of the pandemic and before the vaccine was created, lockdown and other safety measures were implemented. In this context, the socio-economic burden of the disease was, in our opinion, a major issue because we always considered that COVID-19 would have a hard impact on human beings and that that impact would be the most prominent effect of the pandemic. In consequence, when designing this Special Issue, we hoped to receive papers with ”tales from the field” that would describe the mentioned socio-economic burden. Therefore, it was deeply rewarding to receive so many contributions of very good quality that ended up composing the Special Issue that is reprinted here. This reprint includes the 11 papers that made the Special Issue on the socio-economic burden of the disease regarding the COVID-19 pandemic, published in the Healthcare journal in 2022. These 11 papers provide a unique set of reflections regarding the pandemic and its consequences and should be read by everybody interested in the topic. We sincerely thank all the authors and reviewers for the work they produced and we congratulate them for their success. We believe that this reprint of the Special Issue contributes to the understanding of the major consequences of COVID-19 in society. Crucially, the reprint includes papers on global perspectives but also national cases and also sector-specific cases. Finally, we hope the legacy of this volume will be long-lasting and that the papers it contains will be quoted and cited for many years to come. Eduardo Tom´e, Thomas Garavan, and Ana Dias Editors ix Healthcare 2022,10, 324 4. Methods and Results This study adopted a spatial-based and machine learning regression method to analyze the correlation between COVID-19 cases, deaths, and independent variables. The spatial method was applied to analyze the correlation and to present it visually on maps with variation of correlation degree. ML regression model is a strong tool that could be used for different topics and purposes, and the cause and analysis is one of them. Moreover, applying several models to compare results is important to find the most suitable model for this study and document it. In this study, the authors used ArcGIS-ArcMap software version 10.3 for GIS analysis and Jupiter software to apply the regression analysis. The method (Figure 2) applied used GIS tools for spatial and Sci-Kit Learn software libraries for machine learning regression, respectively. The GIS regression methods applied four models: the scatterplot matrix graph, spatial autocorrelation (Moran’s I), ordinary least squares (OLS), and the geographically weighted regression. The ML regression method applied four models, and they are linear multioutput regression, K-nearest neighbors of multioutput regression, random forest of multioutput regression, and support vector regression. These models were applied to analyze the correlation between dependent (COVID-19 cases and deaths) and independent variables (med-income, poverty rate, population density, high blood pressure, high cholesterol, obesity, number of healthy food outlets, and number of healthy food outlets). Figure 2. Methodology graph. 4.1. GIS Methods These maps in Figures 3 and 4 present the COVID-19 cases and deaths. In Figure 3, higher numbers of cases are presented in dark blue color. The lowest COVID-19 infections are in the downtown of Greensboro, where it has fewer residential homes than businesses, and the highest are located outside of Greensboro in Summerfield, Gibsonville, Sedalia, Burlington, and Pleasant Garden. In Figure 4, the highest numbers of deaths are ranging between 22 and 33 per each census tract, displayed in blue color, and the lowest numbers of deaths are given 0 to 3 per each census tract in yellow color. The COVID-19 deaths low numbers are reported in Greensboro and the high mortality reported out of the city. An observation from this distribution could be about people’s education and the mask enforcement in large stores or offices. After that, scatterplot matrix graph in Figure 5 presents the interaction between COVID-19 cases and independent variables. The graph illustrates some positive and negative correlations and no correlation. Positive correlations include obesity with poverty and high blood pressure. Negative correlation is presented between obesity and med-income variables. However, there is no apparent strong correlation observed between COVID-19 cases and other variables through this scatter matrix visualization. 6 Healthcare 2022,10, 324 Figure 3. COVID-19 cases in Guilford County. Figure 4. COVID-19 deaths distribution. 7 Healthcare 2022,10, 324 Figure 5. Scatterplot matrix graph using cases as dependent variable. The scatterplot matrix graph is also applied to COVID-19 deaths as a dependent variable. The graph (Figure 6) also presents no correlation between COVID-19 deaths and variables. Negative correlations are presented between med-income and poverty and obesity. Figure 6. Scatterplot matrix graph using deaths as dependent variable. 8 Healthcare 2022,10, 324 After that, we applied the spatial autocorrelation (Moran’s I) to find the cluster of cases and deaths on some census tracts. The spatial autocorrelation is applied by this equation: I=n S0 ∑n i=1∑n j=1Wi.jZiZj ∑n i=1Z2 i (1) In Equation (1) Zi is the deviation of an attribute for feature i from its mean ( Xi−X ). The Wi . j is the spatial weight between feature Iand j , and n is equal to the total number of features. The S0 is the aggregate of all spatial weight. After applying the equation, results are presented in Figures 7 and 8. Figure 7 illustrates that COVID-19 cases are significantly clustered in Guilford County, which means there is high dependency of output and independent input variables. Figure 7. Spatial autocorrelation for COVID-19 cases. Figure 8. Spatial autocorrelation for COVID-19 deaths. 9 Healthcare 2022,10, 324 In Figure 8, the spatial autocorrelation concluded that the cluster of COVID-19 deaths is a result of random chance, which encourages the investigation further on different variables. The Moran’s summary of COVID-19 cases and deaths by the Moran’s I spatial autocorrelation is in Table 1 below. Table 1. OLS results for COVID-19 cases and deaths. Measures COVID-19 Cases COVID-19 Deaths Moran’s Index 0.118617 0.005965 Expected Index −0.009259 −0.009259 Variance 0.000575 0.000551 Z-score 5.3314423 6.48788 p-value 0.000000 0.516475 Next, local Moran’s was applied based on this formula: Ii=χi−X S2 i ∑n j−1, j=iwi.j(xj−X)(2) In Equation (2), n is the total number of features, and χi is the attribute for feature i . Moreover, wi.j is the spatial weight between feature i and j . The output of this equation is presented in Figures 9 and 10. Figure 9, the local Moran’s on COVID-19 cases, presents tracts with high case numbers and its correlation with a high number and percentage of variables in the south of Greensboro and east of Guilford County. The pink patch represents high cases of COVID-19 with an increase in variables. The red patch represents high cases and low variables correlation. The blue patch illustrates tract with low cases number with low variables in Greensboro downtown. In Figure 10, the local Moran’s on COVID-19 deaths is presented with the correlation of variables in each tract. The red patch represents high mortality with low correlation with variables, and the pink patch represents high mortality number with high variables in the north of Greensboro. Figure 9. The local Moran’s on COVID-19 cases in Guilford County. 10 Healthcare 2022,10, 324 Figure 10. The local Moran’s on COVID-19 deaths in Guilford County. Then, OLS was applied to examine dependent and independent variables. OLS is a linear regression to perform a prediction or detect relationship between dependent and independent variables. We examine COVID-19 cases as a dependent variable with all independent variables. This OLS model uses the equation below: Y=β0+β1X1+β2X2+βnXn+ Ɛ (3) where Y is the dependent variables, β is coefficients, X is explanatory or independent variables, and Ɛ is random error. In Figure 11, red patches represent areas with higher COVID-19 cases than the model predicted, and the blue shaded census tracts illustrate areas with lower COVID-19 cases than the model expected. In this model, the multiple R square was 0.358946, and the adjusted R-square was 0.307662. The Akaike’s information criterion (AICc) was 1412.247528. The joint F-statistic was 0.000000, which was a significant result. The Jarque–Bera statistic [g] was 1.511785, which indicates that the independent variables have an influence on the dependent variable. The joint Wald statistic [e] was significant and computed as 0.000000. The Keonker (BP) statistics, which determine if the independent variables have a consistent relationship to the dependent variable, was 0.009854, also significant, but the relationship is not consistent. In Figure 12, red patches represent areas with higher COVID-19 deaths than the model predicted, and the blue shaded illustrates areas with lower COVID-19 deaths than the model predicted. In this model, the multiple R square was 0.159614, and the adjusted R-square was 0.092383. The Akaike’s information criterion (AICc) was 685.908921. Joint F-statistic was 0.021994, which was a significant result. The joint Wald statistic [e] was 0.000000 as a significant result. The Keonker (BP) statistics determine if the independent variables have a consistent relationship to the dependent variable, and it was 0.388493, which was not significant. The Jarque–Bera statistic [g] was 0.000000, which is significant and means the model is biased and needs further investigation. 11 Healthcare 2022,10, 324 Figure 11. OLS on COVID-19 cases in Guilford County. Figure 12. OLS on COVID-19 deaths in Guilford County. Based on the independent variables’ coefficient of the OLS, variables with higher coefficients than 7.5 will be applied in the GWR. These variables are high cholesterol, high blood pressure, and healthy food outlets. In Figures 13 and 14 GWRs were applied on COVID-19 cases and deaths to visualize the correlation with independent variables by applying this equation: y=B0+B1x+E(4) 12 Healthcare 2022,10, 324 Figure 13. Geographically weighted regression on COVID-19 cases. Figure 14. Geographically weighted regression on COVID-19 deaths. In this equation above, the coefficient B1 illustrates the increase in y because of one -unit increase in x. This map shows less tract with high correlation and more with medium correlation. In Figure 13, the map presents the correlation between the dependent and independent variables. Red patches, which represent high correlation, are in east of Gilford County in the tracts 012803, 015300, and 017200. In Figure 14, the map presents the correlation of COVID-19 deaths with variables (high cholesterol, high blood pressure, 13 Healthcare 2022,10, 324 and health food outlets) and presents correlation degrees in color shades. The highest correlation of COVID-19 deaths with the variables is presented on the tracts 015703, 012604, and 013700. 4.2. ML Regression Results and Discussion This study adopted machine learning techniques to investigate the correlation by applying both linear and nonlinear regression models. Linear, multi-output linear, random forest, and K-nearest neighborhood regression models were applied to investigate the data. All models investigate all variables at the same time, but linear regression investigates single output at a time. These four models were applied to evaluate their results. These models are predicting the values of the dependent variables, such as COVID-19 cases and COVID-19 deaths, with the correlation of independent variables of med-income, poverty rate, population density, number of healthy food outlets, and number of un-healthy food outlets. The dataset was divided into 80% training and 20% testing for multioutput model development. The training set contained eighty-seven (87) observations and twenty-two (22) observations in the testing set, and two different metrics: root mean square (RMS) and R-squared (R 2 ), which were used to evaluate the models developed. The implementation of multioutput and multiple linear regression models were done with the Sklearn package in Python and MATLAB 2020a, respectively. The default parameters for the multioutput regression models were used in Table 2. Table 2. Regression models’ parameters. Model Parameters Linear Regression Model copy_X = True,fit_intercept = True,n_jobs = None,normalize = False. Random Forest Regression Model bootstrap = True,ccp_alpha = 0.0,critrion = ‘mse’,max_depth = None,max_features = ‘ato’,max_leaf_nodes = None,max_saples = None,min_impurity_decrease = 0.0,min_imprity_split = None,min_samples_leaf = 1,min_samples_split = 2,min_weight_fraction_leaf = 0.0,n_estimtors = 100,n_jobs = None,oob_score = False,random_state = None,verbose = 0, warm_start = False) K-Nearest Neighbor Regression Model lgorithm’:’auto’,’leaf_size’:30,’metric’:’minkowski’,’metric_params’: None, ‘n_jobs’: None,’n_neighbors’: 5, ‘p’: 2, ‘weights’: ‘uniform’ The equation below is derived in the linear regression model. In the equation, coefficients of variables were computed based on the linear regression model. Y = 0.53 + 0.194 ×1−0.251X2+ 0.887X3−0.915X4−0.0996X5+ 0.315X6−0.026X7(5) The degree of linear association between all variables is computed by the Pearson correlation coefficient (R 2 )-scores in the correlation matrix heatmap format in Figure 15. The results could be read in three directions: R values close to 1 show a positive relationship, and R values close to − 1 illustrate negative relationships, but results close to zero have no linear relationships. It can be observed in the heatmap (Figure 13) that there is a positive correlation between obesity and poverty (R 2 = 0.74). There is a high positive correlation between high cholesterol and high blood pressure (R 2 = 0.82). Furthermore, there is a positive correlation between obesity and high blood pressure (R 2 = 0.77). Moreover, there is a strong negative correlation between obesity and med-income (R 2 = − 0.7), and a negative correlation between income and poverty (R 2 = − 0.75). There is no correlation between COVID-19 cases and health issues (obesity, high cholesterol, and high blood pressure). Moreover, there is no correlation between unhealthy food outlets, healthy food outlets, and health issues. 14 Healthcare 2022,10, 324 Figure 15. Correlation matrix with heatmap. From the tables’ results below (Tables 3 and 4), the authors applied and compared the regression models results. The COVID-19 cases as a dependent variable have the highest value of R 2 -score as 45% by the application of linear regression for multioutput regression model, and COVID-19 deaths had a higher value of 60% by the application of support vector regression model. The high correlation R 2 -scores of COVID-19 deaths and variables were also presented by the GIS spatial autocorrelation as clustered distribution in Figure 7. These regression models’ results indicate that independent variables (med-income, poverty rate, population density, number of healthy food outlets, and number of unhealthy food outlets) have more influence on the dependent variable COVID-19 deaths than COVID cases. Table 3. R-square value of regression models. Root Mean Square Error Models CVID-19 Cases COVID-19 Deaths Linear regression for multioutput Regression 0.146 0.141 K-nearest neighbors for multioutput regression 0.208 0.147 Random forest for multioutput regression 0.186 0.175 Support Vector Regression 0.168 0.127 15 Healthcare 2022,10, 315 will promote equality, stimulate regional output, diminish regional unemployment, and increase income levels. Investments into less developed regions suggest higher multiplicative effects, suggesting the use of investment into regional healthcare sectors as a tool for equal regional development. Although healthcare spending has been growing for decades in a large number of countries, there were attempts to cut the costs, especially in times of public finance constraints. Empirical data and the literature give grounds to believe this strategy does not bring the desired result in either the economic perspective, as austerity measures do not promote but rather harm the recovery (Darvas et al. [ 14 ]), nor in the health outcomes, as the avoidable mortality can be affected. A study by Arcà et al. [ 28 ] reveals that, even in countries with relatively low avoidable mortality, spending cuts in healthcare can hurt survival. Furthermore, the procyclicality matters, as reducing procyclicality of government health expenditure by keeping them in bad times may generate substantial health gains (Liang and Tussing [29]). The literature extensively explores economic effects of the pandemic along with the policy measures to reduce them and the damage to the national and global economy. These measures arise from monetary, macroprudential, and fiscal policies. Applied policies include relief measures, recovery policies, and international coordination measures and are stated to reduce the consequences independently or as a combined mix of measures [ 19 ]. However, while such a research approach explores policy options to act against the consequences of an economic crisis caused by the pandemic, our approach is innovative in moving the perspective to the options of economic policy to reduce contributing factors of the severity of the pandemic outcome. The present study is thus original in the following ways. While the economic literature often takes the perspective of empirically exploring an individual determinant or some determinants which are ex-ante, selected based on theoretical grounds and the impact on the health outcomes during a specified time frame, we take an innovative point of view. We await to identify areas where an impact on the health outcomes in the case of the COVID-19 pandemic originated and can be affected by economic policy measures in the shortor long-term perspective to enhance reliance to possible future health crisis. The paper is organized as follows. After highlighting the relevant economic characteristics and exploring grounds for economic recovery in the first section, we present the data sources and methods used in the study. Next, Section 3 gives technical results and their interpretation regarding the research question. Finally, Sections 4 and 5 complete with the discussion and conclusions, respectively. 2. Materials and Methods Although the COVID-19 epidemic is not yet over, already a lot of data is made available by statistical offices, international organizations, national governments and their public health institutes, and many other organizations. Initially, we have collected 171 data variables for 197 countries, from 2017 to 2020, to ensure that, in some minor cases where the most current data were not available, the latest possible data, or an estimation, were taken. Collected data considered economic, infrastructure, cultural, health, and other areas. Economic variables were obtained from World Bank Open Data (https: //data.worldbank.org/, accessed on 10 December 2020), IMF’s World Economic Outlook Database (https://www.imf.org/en/Publications/WEO/weo-database/2020/October, accessed on 10 December 2020), Trading Economics portal (https://tradingeconomics. com/indicators, accessed on 10 December 2020), and FDI Attractiveness Index website (Ben [ 30 ], accessed on 10 December 2020) (http://www.fdiattractiveness.com/ranking2020/, accessed on 10 December 2020). Infrastructure variables were fetched from Enerdata (https://yearbook.enerdata.net/, accessed on 10 December 2020) and ITU (https: //www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx, accessed on 10 December 2020), while other relevant cultural variables from Wikipedia, ETH’s KOF (https://kof. ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html, accessed on 10 December 2020) (Gygli et al. [ 31 ] and Dreher [ 32 ]), and Google Mobility (GM) web22 Healthcare 2022,10, 315 site (https://www.google.com/covid19/mobility/, accessed on 10 December 2020). As GM data were reported as high frequency (daily) data, basic transformation for integration with the low-frequency data (others) were necessary. First, the average values of GM data during the first corona-virus outbreak (1 March–1 May 2020) and during the second outbreak (last two months prior to 6th December 2020) were calculated. Two vectors of six categories (retail and recreation, supermarket and pharmacy, parks, public transport, workplaces, and residential) were built in this way. Next, the average between the two built vectors was taken to form a single, consolidated, composite indicator. There, it was found that the retail and recreation category showed as most relevant here. Variables on health outcomes were obtained from WHO’s Global Health Observatory data repository https://apps.who.int/gho/data/node.main (accessed on 10 December 2020) and Nextstrain https://nextstrain.org/ncov/global (Hadfield et al. [ 33 ], accessed on 10 December 2020). These were also categorized as high frequency data (number of infected, dead, and recovered people and number of clade mutations) and were recorded at the day of beginning the research, i.e., 10th December. Again, these required specialized treatment, such that composite indicators were built. The rest of the variables came from a consolidated web portal, Our World in Data https://ourworldindata.org/charts (accessed on 10 December 2020), which holds datasets of different data providers. After building a complete (consolidated) dataset as a combination of high and low frequency data, missing data were found such that cleaning of dataset was necessary. Two versions of reduced datasets were generated. In the first, there were data for 78 countries with 11 variables altogether. In the second, by reducing the number of countries, 13 more variables could be included. The list of the explanatory variables is as follows and can be divided into several groups: virus characteristics (COVID19 cases—cumulative total, COVID-19 virus clade 20A, and COVID-19 virus clade 20B), population characteristics (share of population older than 65, share of the population living in urban areas, mean BMI (male and female)), equality characteristics (female employmentto-population ratio and Gini index of consumption), healthcare sector characteristics (share of public healthcare sector and the Healthcare Access and Quality Index), national economy characteristics (GDP per capita and PPP, i.e., constant 2011 international $, High-Tech export (share of manufactured exports), FDI country attractiveness, and share of the agriculture sector), and cultural characteristics (Google mobility measures). Additionally, as dummy variables, we included the world regions. Both reduced datasets were generated to the best extent, compromising the number of variables and number of countries to have a good mix of highand low-income countries. Finally, all the variables were standardized before use in models by subtracting the mean and dividing by the standard deviation. As the dependent variable we used data on the number of COVID-19 deaths from WHO’s COVID-19 Dashboard as the variable indicating the severity of the COVID-19 epidemic outcome in an individual country. All gathered data was prepared in pre-processing step (e.g., logarithmic transformation) and analyzed in order to prepare for estimation of regression models. In the first two models, the least squares method was used. In the third one, the Huber-White-Hinkley estimator was used. We used software package EViews 10+ (Enterprise Edition, 64-bit, IHS Global Inc., Irvine, CA, USA, 2018) for the model estimation. A limitation to the study has to be noted here. Although we have taken unified data sources across countries, different inconsistencies in the methodology of collecting data can be found, e.g., number of dead due to COVID-19 (as a main source of implication) is not uniquely defined across countries. The full extent of COVID-19 outcome will be possible to be evaluated when all statistical data in full range and reliability will be available. 3. Results Based on empirical evidence, we considered estimations on multiple regression models to draw an integral framework for identification of areas, where the determinants of severity of COVID-19 outcome came from. Although the results depend on the limited selection of countries and variables, both logarithm–linear and linear–linear models suggested reasonably-connotated connections. Multiple models were estimated instead of one, and 23 Healthcare 2022,10, 315 framework comprises three models and unites the findings altogether. This is presented in Figure 1. We chose the presented three models over other experimental models as they at best met the criteria of high explanatory power, expressed by high levels of coefficient of determination (R-squared). However, the ability to further improve the study’s econometric quality was impacted by our research aim of including the biggest possible number of countries and the widest possible selection of the explanatory variables. Nevertheless, the final models exhibit high values of R-squared, especially due to the fact that we are dealing with cross-section and highly heterogeneous data. Model 1 Model 2 Model 3 Covid 19 outcome variable LOG(Deaths - cumula�ve total) Deaths - cumula�ve total Deaths - cumula�ve total Determinants of the Covid 19 outcome Coefficient Prob. Coefficient Prob. Coefficient Prob. South America - 1.381 *** Regional characteris�cs No policy measures possible Africa - 1.856 *** Asia - 1.894 *** - 0.639 * - 0.7105 * Oceania - 3.360 *** - 2.828 *** - 4.8998 *** Covid 19 Cases - cumula�ve total 0.307 *** Virus characteris�cs No policy measures possible Covid 19 virus clade _20A 0.024 0.030 0.4856 Covid 19 virus clade _20B 0.250 * 0.240 * 0.6210 *** Share of popula�on older than 65 - 0.587 ** Popula�on characteris�cs Longand mid-term policy measures possible Share of the popula�on living in urban areas 1.713 *** 2.0748 ** Mean BMI (male and female) 3.872 ** 9.9473 *** Female employment-to-popula�on ra�o - 1.266 *** Equality characteris�cs Long-, mid-, and short-term policy measures possible Gini index of consump�on - 1.814 ** - 3.9628 *** Share of public health care sector - 0.478 * - 0.9352 ** Health sector characteris�cs Long-, mid-, and short-term policy measures possible Healthcare Access and Quality Index - 3.9142 ** GDP per capita, PPP (constant 2011 interna�onal $) - 0.393 * Na�onal economy characteris�cs Long-, mid-, and short-term policy measures possible High-Tech export (share of manufactured exports) - 0.350 ** - 0.7123 *** FDI country a�rac�veness 5.063 *** 6.7496 *** Share of the agriculture sector - 0.393 * Google mobility measures 0.451 *** 0.4016 *** Cultural characteris�cs Short-term policy measures possible Constant 0.101 - 5.472 *** - 11.4865 *** Sample 78 61 61 R-squared 0.734 0.664 0.7918 F-sta�s�c 16.538 *** 8.794 *** 15.2122 *** Method Least Squares Least Squares Huber-WhiteHinkley es�mator Areas of possible policy measures Types of possible policy measures Complexity of possible policy measures Figure 1. ‘*’ = p-value lower than 0.10, ‘**’ = p-value lower than 0.05, ‘***’ = p-value lower than 0.01. Economic policy framework for determining pandemic outbreak measures. Source: own calculations and figure presentation. In this research, there was a challenge of heteroscedasticity. In the modelling phase, we controlled for the heteroscedasticity by different approaches reflected in the three models. In the first, we chose to use the logarithmic value of the dependent variable. In the third, we took another approach, namely, a heteroscedasticity robust estimator: Huber–White– Hinkley estimator. For a benchmark, we did not apply any adjustments in the second model due to the heteroscedasticity. When interpreting the results, another fact should be taken into account, namely the possible presence of multicollinearity. In the initial modelling step, multicollinearity has impacted the selection of explanatory variables severely. After the selection, we empirically found that a possible threat of multicollinearity was still indicated in the model. Namely, some of the regression coefficients exposed the signs (connotations, i.e., − /+) opposite as expected, which we interpreted exclusively as a consequence of multicollinearity. Still, we followed a common econometric rule that a multicollinearity is not a reason for omitting the model. There were dummy variables included in the models for regions that statistically significantly deviated from the global average. We believe this is due to the huge differences in the initial position at the beginning of the epidemic in individual countries. However, when considering the robust estimator, the only significant results remain the dummies for Asia and Oceania, where very restrictive measures against the spread of the virus were applied. The results on the estimated econometric models reveal some interesting findings. Among contributing factors to a more severe epidemic outcome, higher population mobility, a higher level of the population living in urban areas, a weaker physical condition of the population, and the openness of the economy all featured. On the other hand, a positive impact came from a higher share of the primary economic sector in the ecosystem structure (agriculture) and high economic development measured as High-Tech export. Additionally, 24 Healthcare 2022,10, 315 impact came from a higher share of the primary economic sector in the ecosystem structure (agriculture) and high economic development measured as High-Tech export. Additionally, the importance of public healthcare was revealed, as better healthcare access and quality notably contributed to a more favourable epidemic outcome. The results suggest there are multiple areas which determined the severity of the COVID-19 outcome in individual countries: • regional characteristics; • virus characteristics; • population characteristics; • equality characteristics; • healthcare sector characteristics; • national economy characteristics; • cultural characteristics. When examining the areas closely, the overall analysis of all three models suggests that there are three groups of factors which influence the outcome of the pandemic in individual countries. In regards to the economic policy, these groups differ and can be listed as follows: • areas where factors cannot be influenced by economic policy measures; • areas where factors can be influenced by longand mid-term policy measures; • areas where factors can also be influenced with short-term policy measures and prompt results are possible. The analysis of the framework reveals that economic policy measures cannot influence the regional characteristics, e.g., where the individual country is placed, as well as the virus characteristics, e.g., virus clade present in the particular country. The other two groups of factors are relevant for the economic policy, as they might be influenced by long, mid-, and short-term policy measures. The area of population characteristics could be addressed with midand long-term measures, and could be directed to the population structure, ranging from living conditions such as urbanization up to ageing structure or physical characteristics of the population. The group of measures, likely to be less complex than those previous, would be long-, mid-, and short-term policy measures and would aim to favourably enhance the equality characteristics of the society. The understanding of equality, in this sense, is broad and includes the gender impact, the labour market conditions, and the distribution of wealth, also on the regional level. The next area of possible economic policy measures would be undertaken aiming at changes of the healthcare sector characteristics. These can be impacted with combination of long-, mid-, and short-term measures, therefore it also includes structural characteristics of the sector, including the capacity, quality, and accessibility of the services. The area of characteristics of the healthcare sector includes the structure according to the public and private share of the healthcare sector. The results are in favour of a larger share of the public healthcare sector. Our results also indicate that the characteristics of national economy had an impact on the severity of the pandemic outcome. By economic characteristics, not only the level of economic development measured by e.g., GDP per capita is meant, but the structure of the economy, namely the sectoral structure, is encountered. The level of innovation and structural changes will be at the forefront of this area of economic policy measures. Furthermore, short-term policy measures could influence the area of cultural characteristics, among which the mobility of the population is limited. The complexity of measure will gradually increase. The less complex measures will be applied at the area of population characteristics, while the most complex measures are expected to be applied at the area of national economy characteristics and the cultural characteristics. Based on empirical findings, we propose a mix of possible economic policy measures directly or indirectly linked to the healthcare sector. This includes promoting public healthcare, ensuring crisis capacities, and access to quality healthcare. On the other hand, state and obligatory health insurance premiums should also account for individuals’ decisions, 25 Healthcare 2022,10, 315 resulting in higher healthcare costs, e.g., non-vaccination once a vaccine is available. Alternatively, participation in healthcare costs for non-vaccinated could be applied and used to finance scaling-up the capacities. To encourage a resilient economy for the future, economic policy must implement policy measures, based on empirical findings on characteristics of economic structures and multiplicative effects as well as actual lessons learned from the economic consequences of COVID. Furthermore, it has been argued that standard fiscal stimulus might be less effective than normally expected due to muted Keynesian multiplier feedback (Guerrieri et al. [ 10 ]). Additionally, as indicated in the literature (Bekö et al. [ 15 ]), the impact of the healthcare sector seems to remain stable throughout the business cycle, which suggests the predictability of economic measures. In the end, economic recovery is costly. Instead of burdening future generations due to higher public debt, financing sources should be at the cost of individuals who behave opportunistically in the epidemic crisis. State sovereignty includes fiscal measures; therefore, finding these additional sources in a form of a COVID-19 tax could be justified. 4. Discussion As with any study, the limitations have to be considered for proper interpretation of the results. In this study, limitations arise from two perspectives: the data and the methods. Although we have taken unified data sources across countries, we found inconsistencies in their data collection approaches, e.g., number of deaths due to COVID-19 is not uniquely defined across countries. Further, the data availability was limited in the sense that for an individual variable for some countries there were missing values. Consequently, it has led to the trade-off between a larger number of variables or a larger number of included countries. The results are thus impacted by the choice we made in this perspective and might differ from models, where we would either include fewer explanatory variables but even more countries or contrary, more explanatory variables, and fewer countries. Further, regarding the study design, standard testing of policy impact (e.g., treatment effect models, but also Granger causality test) was according to the nature, quality, and availability of data not possible to apply. Additionally, because the pandemic and the applied measures have not yet come to an end, other econometric approaches as what we went for did not seem reasonable in our case. Again, we tried to make the study as broad as possible (in the number of countries included and in the range of variables included), which also impacted the possibilities of applied econometric approaches. The obtained scientific implications thus are based on a starting period of the COVID19 pandemic. Later, it will be possible to evaluate the full extent of the dependencies analysed here in relation to COVID-19, once all statistical data in full range and reliability is be available. The study gives several scientific implications. We found that multiple factors, which determined the severity of the COVID-19 outcome in individual countries, arise from regional, virus, population, equality, healthcare sector, national economy, and cultural characteristics. Along with the scientific implications presented in detail in the results section, another important finding was revealed by this study, namely the relevance of high-frequency data. In our study, we used Google mobility data as one explanatory variable, but many more could be relevant in the future. High-frequency data, in general, emerged as a result of the use of modern information technologies. However, two aspects of their applicability in science have to be given attention: first, appropriate methodological approaches capable of dealing with such data, and secondly, the availability of the data to the scientific community. Next, we turn to the economic policy framework, which is serving as an identification matrix for policy implications. We identified several areas that could be relevant for the severity of the epidemic outcome. This section discusses several ideas that suggest economic policy measures to impact the severity of the epidemic outcome favourably. Our results suggest that national economy characteristics matter; thus, we discuss the policy measures which would address them. The GDP per capita and high-tech export could be influenced. Financial data show that the healthcare sectors’ stocks outperformed 26 Healthcare 2022,10, 315 most others. The research and development in the healthcare sector industry promotes a high level of innovation which not only contributes to the affordable healthcare, but promotes economic development with high value-added and creates jobs for highly skilled professionals. Encouraging investments in innovative industries (healthcare, pharmaceutical, biotech, and associated industries) could thus be a good way to influence the variables which are found in the group “national economy characteristics” in our framework. Next, we argue that post COVID-19 investments should encourage R&D in artificial intelligence (AI). Innovation and transformation accelerate economic growth and promote resilient economic systems. AI can already be applied as the first stage in diagnosing less severe cases, thereby releasing capacities (AbuShaban [ 2 ]). Investing in AI in the healthcare sector will have huge spill over effects, as this means investment into AI professionals, companies developing AI solutions, and implementation of these solutions in other sectors, making the economy future-ready. Promoting R&D in AI and AI usage in healthcare could have a favourable impact on the variables in the groups “national economy characteristics” as well as “health sector characteristics”. Additionally, AI can be seen as a convenient tool for stipulation of a healthy lifestyle (in smart watches, sensors, and wearables), which importantly lowers COVID-19 severity (we noticed a significant connection between physical condition measured by BMI and cumulative total). We further discuss the measures aiming to change the characteristics of the healthcare sector, especially due to the high relevancy of the regression variable “Healthcare Access and Quality Index” ( − 3.9142 **), as indicated in the third model of the variable. Additionally, the framework from this study indicates that the private–public healthcare matters. As the healthcare sector must be part of the critical infrastructure, the government and the private sector should establish a relationship between each other to encourage the necessary cooperation (see also AbuShaban [ 2 ]). Networks of regional providers are more critical to community recovery than centres. Public healthcare providers are more suitable to provide sufficient backup capacities in areas which are not profitable. If public healthcare providers operate in profitable healthcare services, profits can be used for covering losses from operating in non-profitable services. For example, reserving and maintaining capacities for national medical emergencies is costly and does not gain profits. Transformation of healthcare systems with more flexibility can contribute to provide access to quality healthcare. Both flexibility in physical capacities (AbuShaban [ 2 ]) and medical staff flexibility (Ferreira et al. [ 34 ], Casha and Casha [ 35 ]) should thus be addressed. The healthcare sector of many countries is suffering from medical stuff shortages resulting from emigration of medical professionals (Ferreira et al. [ 34 ], Casha and Casha [ 35 ]). We argue that there is the need to design policy measures that mitigate the intention of healthcare professionals to emigrate. Temporary deficits on the labour markets can be solved by encouraging short-term medical staff mobility. Long-term shortages should be addressed by economic policy measures. However, one must notice that mobilities in general are not appreciated, as the “Google mobility measures” exposes positive regression coefficients. Additionally, we suggest reconsidering “state aid” in industries that negatively affect health and environment in any economic policy action. Therefore, capital injection measures should be considered in industries according to economic, environmental, and health criteria. This would have long-term effects on health and environment and would make economies more resilient to future disruptions while also contributing to equity. 5. Conclusions In this study, we support the thesis that innovative measures of economic policy should be applied in the after-COVID-19 period. These measures should differ from traditional ones, be applied in advance of an epidemic, and thus to support economic and social ecosystems to become more resistant to current and future epidemic crises. Many of the proposed measures directly or indirectly concern the healthcare systems and healthcare sector, aiming to impact its characteristics, such as public-vs-private healthcare or the access and quality of the healthcare provided. Economic policy measures could thus 27 Healthcare 2022,10, 315 promote new technologies in healthcare, sectoral staff flexibility, or reinforce decentralised (regional) public health services providers. A central element of this study, the innovative identification matrix, which combines unbiased econometric results with remediation, could be populated as a unique policy framework, either for latest pandemic or any similar outbreaks in future. However, such a policy framework is not only to be used for identifying pandemic outcomes, but also when the final data on the pandemic outbreak outcomes become available, to make accurate and reliable predictions of the effect on individual economic, health, and social life factors. In the end, its application in policy design could contribute to modern societies’ efforts on equality, human rights, and social cohesion. We suggest further research on this topic. With the passage of time, the data on the longer time frame of the COVID-19 pandemic period will be available. This would enable a panel-based econometric approach instead of a cross-sectional one. In doing so, both the range of included characteristics of the countries and the genetic changes of the virus could improve the model and reveal new dependencies in the examined countries’ characteristics to the severity of the pandemic. Additionally, as we discussed the scientific potential of high-frequency data, future research could include them in investigations. The latter would enable the detailed study of interaction between high-frequency data variables and the genetic profile of the virus. Author Contributions: Conceptualization, T.J.; methodology, T.J.; software, T.J. and D.F.; validation, T.J. and D.F.; formal analysis, T.J. and D.F.; resources, D.F.; data curation, D.F.; writing—original draft preparation, V.J.; writing—review and editing, T.J. and V.J.; visualization, T.J.; and supervision, T.J. All authors have read and agreed to the published version of the manuscript. Funding: The authors acknowledge the financial support from the Slovenian Research Agency (Research core funding No. P5-0027). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Publicly available datasets from multiple sources were analysed in this study. 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Moving health professionals as an alternative to moving patients: The contribution of overseas visiting medical specialists to the health system in Malta. Health Policy 2020,124, 519–524. [CrossRef] 29 healthcare Article The Economic and Psychological Impacts of COVID-19 Pandemic on Indian Migrant Workers in the Kingdom of Saudi Arabia Mohammed Arshad Khan 1,*, Md Imran Khan 2, Asheref Illiyan 2and Maysoon Khojah 1 Citation: Khan, M.A.; Khan, M.I.; Illiyan, A.; Khojah, M. The Economic and Psychological Impacts of COVID-19 Pandemic on Indian Migrant Workers in the Kingdom of Saudi Arabia. Healthcare 2021,9, 1152. https://doi.org/10.3390/ healthcare9091152 Academic Editors: Eduardo Tomé, Thomas Garavan, Ana Dias and Pedram Sendi Received: 21 July 2021 Accepted: 27 August 2021 Published: 3 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Accounting Department, College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia; [email protected] 2Department of Economics, Faculty of Social Sciences, Jamia Millia Islamia (A Central University), New Delhi 110025, India; [email protected] (M.I.K.); [email protected] (A.I.) *Correspondence: [email protected] Abstract: The ongoing Coronavirus disease 2019 (COVID-19) pandemic has changed the working environment, occupation, and living style of billions of people around the world. The severest impact of the coronavirus is on migrant communities; hence, it is relevant to assess the economic impact and mental status of the Indian migrants. This study is quantitative in nature and based on a sample survey of 180 migrant workers. Descriptive statistics, chi-square test, dependent sample t-test, and Pearson’s correlation coefficient were utilized to analyze the surveyed data. The findings of the study reveal, through the working experience of the migrants, that new international migration has reduced due to lockdown and international travel restrictions. It was also reported that the majority of the migrants worked less than the normal working hours during the lockdown, causing a reduction of salary and remittances. Chi-square test confirms that the perceptions of migrants towards the COVID-19 management by the government were significantly different in opinion by different occupation/profession. Majority of the sampled migrants reported the problem of nervousness, anxiety, and depression; however, they were also hopeful about the future. The psychological problem was severe for the migrants above the age of 40, not educated, and with a higher number of family members. Subsequently, the policy implications from the findings of the research can draw attention of the policy makers towards protective measures which need to be implemented to support migrants during the ongoing pandemic. The government should take some necessary steps, such as a financial benefit scheme, to overcome the problems in the reduction of migrant earnings and remittances. The government should not focus only on vaccination and physical fitness of the migrants but also need to find out the cure of the psychological impact arising during the pandemic. Keywords: COVID-19; pandemic; migrants; sample survey; employment status; remittances; migrants’ perception; economic and psychological impacts 1. Introduction The dangerous ongoing pandemic, Coronavirus disease 2019 (COVID-19), reported in Wuhan city, China, in December 2019 and it is caused by a novel coronavirus called SARS coronavirus 2 (SARS-CoV-2) [ 1 ]. The director general of the World Health Organization (WHO) initially declared the spread of coronavirus as a public health emergency of international concern on 30 January 2020. Later on, the WHO declared a pandemic on 11 March 2020 [2]. The novel coronavirus is unique in nature because of high man-to-man transmission and has spread to 176 million people worldwide and caused 3.8 million deaths as of 15 June 2021 [ 3 ]. This pandemic has changed the occupation and living style of billions of people around the world and raised questions of medical facility arrangements of the different countries of the world. The government of China started imposing restrictions and the lockdown in Wuhan city began on 23 January 2020, followed by India Healthcare 2021,9, 1152. https://doi.org/10.3390/healthcare9091152 https://www.mdpi.com/journal/healthcare 47 Healthcare 2021,9, 1152 on 24 March and Saudi Arabia on 25 March 2020 [ 4 ]. The main objective of the lockdown or curfew is to limit the spread of the virus by maintaining social distancing and creating medical facility on war footing. Lockdown; banned public gatherings; suspending religious activities; closure of business, schools, colleges, etc.; curfews; and restriction or suspension of all the travel domestically as well as internationally were followed by the majority of countries as preventative measures. The government of Saudi Arabia had suspended all its international flights on 15 March 2020 and resumes after 14 months on 17 May 2021; however, the suspension of flights will continue to 13 countries, including India due to the second wave of corona virus [5]. The COVID-19 pandemic is not only a health emergency but also a labor market and economic crisis because of its effects on the business status of millions of individuals. The Saudi health ministry took the initiative to provide free corona vaccine and also offered free corona screening and health care services to all of its citizen, including migrants workers, and made vaccine compulsory for the health care workers participating in Hajj and Umrah (Islamic pilgrimage to Mecca) initially and later on made it compulsory for all male and female private and public sector workers to attend the workplace [ 5 – 7 ]. COVID-19 immunization will be required to participate in any socio-cultural, commercial, economic, entertainment, or supporting affairs in Saudi Arabia from 1 August 2021 [7,8] . The government of Saudi Arabia is strict towards the enforcement of COVID-19 regulations to reduce its spread and violators are fined between Saudi Riyal (SAR) 10,000 to SAR 100,000; however, a second wave of COVID-19 hit the country in the beginning of February 2021 [9]. According to Indian Census-2011, India had 45.6 crore migrant population (38%) and, according to the recent report published by “United Nations Department of Economic and Social Affairs—2019”, India continues to have the maximum of its people (17.5 million) living overseas and highest remittance receiving country (USD 78.6 billion). Saudi Arabia is the third top remittances sending country (USD 36.1 billion) in the world and ranked third (13 million) in largest number of international migrants in the world. India–Saudi Arabia shifted from the tenth (2000–2010) to seventh largest bilateral migration corridor in the world [10,11]. The COVID-19 pandemic had reduced the new international migration and increased the returnee migrants, which happens to be the first time in recent history. According to an estimate by World Bank, a total of 6,000,000 migrants were evacuated through special flights (Vande Bharat Mission) and Kerala was affected the most by 4,000,000 returnee migrants. The estimated remittances (World Bank) to India will fall by 9% in 2020 and 14% in 2021 and the flow of foreign direct investment will fall by 36% in 2020; however, India will continue to be the top remittance recipient country globally, with approximately USD 76 billion which will be 2.9% of its Gross Domestic Product (GDP). The monetary emergency accentuated by COVID-19 could be long, profound, and inescapable when seen through a relocation focal point [ 12 ]. The oil rich country and job-rich sector in Saudi Arabia was drastically affected by Corona virus because of the drop in trade, disruption of production, tourism (Hajj and umrah), and hospitality. Lockdown and travel restriction reduce the demand for oil globally, and consequently oil prices had fallen by 50% in March 2020 . To recuperate the economic slowdown, the Saudi government allowed private sector companies to cut the salaries of the workers up to 40% for a period of six month and thereafter could also terminate the contract [ 13 , 14 ]. The majority of the migrant workers in Saudi Arabia are engaged in the construction sector, agriculture, hospitality, and domestic work, which are highly affected by the ongoing pandemic. The acutely affected migrants in the state during the pandemic are domestic workers, low skilled/low-income workers, contract terminated or completed workers, informal workers, women migrant workers, and salaried employees. In this context, the present study attempts to make a deeper analysis of economic and psychological impacts of COVID-19 pandemic on Indian migrant workers in Saudi Arabia. The paper is coordinated as follows: Section 1 is introductory, Section 2 reviews the literature, Section 3 describes the research gap, Section 4 delineates 48 Healthcare 2021,9, 1152 ȱ 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% Below1000 1000Ͳ3000 3000Ͳ5000 Above5000 Remittances(inSAR) AxisTitle RemittancesBeforeandDuringLockdown BeforeLD DuringLD Figure 2. Change in remittances during the pandemic. 5.5. Dependent Sample t-Test Dependent or paired sample t-test is used to compare the differences in the value of same sample at two different times. To test whether the comparison of remittances shown in Figure 2 is statistically significant, a t-test is applied. The null hypothesis was ‘there is no statistical difference in remittances before and during the lockdown of the pandemic’. The mean and standard deviation of remittances before lockdown were estimated to be 1.78 and 0.917, respectively, and during lockdown the mean = 1.62 and Std. Deviation = 0.885. Table 4 describe the results of the paired t-test. T statistics of 3.924 with 179 degree of freedom corresponded to the p-value = 0.000, which is less than 0.05; therefore, we reject the null hypothesis. It means that there is statistically a difference in the remittances before and during lockdown Table 4. Paired Sample t-Test. Paired Sample t-Test 95% Confidence Interval of the Difference Mean Std. Deviation Std. Error Mean Lower Upper t df p-Value Remittances before lockdown—Remittances during lockdown 0.161 0.551 0.041 0.080 0.242 3.924 179 0.000 Source: Calculated by the authors from Google Form questionnaire. 5.6. Migrants’ Perception of the COVID-19 Management by the Government of Saudi Arabia The researchers used six questions to assess respondents’ perceptions of the COVID-19 management by the government of Saudi Arabia. The responses were registered in a fivepoint Likert scale varying from ‘very good’ to ‘very poor’, as presented in Table 5. The migrants were found to be satisfied with the COVID protection related information provided by the government. In total, 51.1% responded ‘very good’, 46.1% responded as ‘good’, only 2.8% believed that ‘average’ information was provided, and none of the migrants responded ‘poor’ or ‘very poor’. Half of the migrants reported ‘very good’ and 48.9% responded ‘good’ in regard to safety measures taken by the Saudi government. Almost the same perception was found in the case of medical facilities provided by the government: 54.4% reported as very good, 44.4% as good, and only 1.1% were average. In response to whether migrants had faced any difficulties sending money to their family during the lockdown period, 51.1% reported ‘very good’, 42.8% reported ‘good’, 3.9% reported ‘average’, and only 2.2% reported ‘poor’ facilities to send money during the lock55 Healthcare 2021,9, 1152 down period. The researchers also asked about the food and other basic facilities provided by the government: 46.7% responded ‘very good’ and ‘good’ separately; however, 6.7% responded ‘average’. Furthermore, 46.1% of the respondents reported ‘very good’ living conditions, 49.4% responded ‘good’, and only 4.4% reported ‘average’ living condition during the lockdown period. Table 5. Perceptions of the migrants towards COVID-19 management by Government of Saudi Arabia. Statement Very Good Good Average Poor Very Poor Total COVID protection related information by Saudi Govt. 51.1% 46.1% 2.8% NIL NIL 100.0% Safety measures taken by Saudi Government 50.0% 48.9% 1.1% NIL NIL 100.0% Medical facility provided by Saudi Government 54.4% 44.4% 1.1% NIL NIL 100.0% Facility to send money to India during lockdown 51.1% 42.8% 3.9% 2.2% NIL 100.0% Food and other facilities provided by Saudi Government 46.7% 46.7% 6.7% NIL NIL 100.0% Living conditions in Saudi Arabia during lockdown 46.1% 49.4% 4.4% NIL NIL 100.0% Source: Calculated by the authors from Google Form questionnaire. 5.7. Migrants’ Perceptions of COVID-19 Management by Government of India The researchers also tried to analyze the perceptions of the migrants towards the facility provided in the evacuation of migrants during the lockdown period. Two questions were asked of the respondents, and responses are presented in Table 6. The first question was related to the help provided by the embassy of India in Saudi Arabia: 33.9% of the respondents reported ‘very good’, 56.1% responded ‘good’, 7.2% responded ‘poor’, and 0.6% responded ‘very poor’. This question was relevant because some migrants may face the problem of visa expiry, passport renewal, or issues related to working contracts. The second question was related to the evacuation of the migrants through transport facilities: 56.1% of the respondent reported ‘very good’, 31.7% reported ‘good’, 8.3% reported ‘average’ and 3.9% responded ‘very poor’. This question was also relevant because the uncertainty which arises due to the spread of Corona virus forces migrants to return to India. Table 6. Migrant’s perceptions towards COVID management by Government of India. Statements Very Good Good Average Poor Very Poor Total Help provided by Embassy of India in Saudi Arabia 33.9% 56.1% 7.2% 2.2% 0.6% 100.0% Transport facility provided by Government of India 56.1% 31.7% 8.3% 3.9% NIL 100.0% Source: Calculated by the authors from Google Form questionnaire. 5.8. Combined Mean of Perceptions Table 7 presents the combined mean of the perceptions towards COVID-19 management by the Government of India and Government of Saudi Arabia. A lower mean value indicates better perceptions. The combined mean of the perceptions towards government of Saudi Arabia is 1.54, which is less than the combined mean value 1.7 of the perception towards the government of India. This result shows that the government of Saudi Arabia managed COVID-19 better than the government of India according to migrants’ perceptions. However, this perception is based on few variables and the responses of the migrants available in Saudi Arabia during lockdown period. 56 Healthcare 2021,9, 1152 Table 7. Combined mean of the perceptions. Perceptions N Minimum Maximum Mean Std. Deviation Perception towards government of India 180 1 5 1.7 0.76 Perception towards government of Saudi Arabia 180 1 4 1.54 0.58 Source: Calculated by the authors from Google Form questionnaire. 5.9. Chi-Square Test A chi-square test was applied to discover the association between perceptions of COVID-19 management by the government across the different professions/occupations. The sample data of occupational structure were classified as Professionals, Technicians, Clerical Support Workers, Service and Sales Workers, Elementary Occupation, Plant and Machine Operator, and others. The null hypothesis was ‘There is no significant difference in the opinion/perception of the migrant workers towards the COVID-19 management by the government across different occupations’, and the alternative hypothesis was ‘there is significant difference in the opinion/perception of the migrant workers towards the COVID-19 management by the government across different occupations. The relationship between these variables was found to be significant as calculated by chi-square value and p-value which is less than 0.05 (5% level of significance) as presented in the Table 8. Therefore, we reject the null hypothesis. It means that there was a significant difference in opinion of the migrant workers towards the COVID-19 management by the government of Saudi Arabia and India. Professionals, technicians, and elementary occupational workers were found to have low negative opinion towards COVID-19 management by the government, especially towards transport facilities provided by the government of India, help provided by the embassy of India, and facilities to send money from Saudi Arabia to India. However, clerical support workers, service and sales workers, and plant and machine operators had highly positive opinions concerning the COVID management by the government. Table 8. Chi-Square Analysis of Perceptions of COVID-19 Management by Govt. Within Different Professions/Occupations. Perceptions Chi Square Value p-Value COVID protection related information by Saudi Government 51.36 0.000 Safety measures taken by Saudi Government 29.18 0.004 Medical facility provided by Saudi government 21.64 0.042 Facility to send money to India during lockdown 39.44 0.002 Help provided by Embassy of India in Saudi Arabia 56.37 0.000 Transport facility provided by Governmentof India 40.14 0.002 Food & other facility provided by Saudi Government 28.86 0.004 Living condition in Saudi Arabia during lockdown 36.02 0.000 Source: Calculated by the authors from Google Forms questionnaire. 5.10. Comparing the Perceptions of Migrants with Other Citizens Perceptions of the citizens or migrants may differ or be the same in different countries, depending upon the infrastructure and decisions taken by the government. Several studies were carried out to investigate the perceptions of the citizens or migrants in a country. For instance, a study on the perception of health care workers during the COVID-19 pandemic in the case of Saudi Arabia was conducted, which confirmed that the majority of the respondents (93.6%) were happy and felt safe in regard to the government decision of lockdown and 94.7% supported the travel restriction imposed by the government [ 33 ]. Similarly, our study also confirms that the majority of the migrants strongly agreed or agreed with the government decision. For instance, 97.2% of the migrants agreed with the information provided by Saudi government related to the protection from Corona virus. In total, 98.8% of the respondents were happy with the safety measures taken by the Saudi 57 Healthcare 2021,9, 1152 government. Another study was conducted to explore the perception of the public of the government of Singapore in relation to COVID-19 related information. The results of the study confirm that majority of the respondents (99.1%) agreed or strongly agreed on the COVID-19 related information provided by the government and 97.9% believed the Singapore news agency [ 34 ]. Another study was carried out in Bangladesh to explore the public perception of government measures related to COVID-19. The result of the sample survey reveals the fact that the majority of the respondents (58%) were not satisfied by the measures taken by the government of Bangladesh. However, 40% of the respondents were found to be satisfied with government decisions [ 35 ]. Our study reveals the fact that the majority of the migrants were satisfied by the decision taken by the Saudi government. Another study for Bangladesh was conducted and revealed the fact that the majority of the respondents (62%) strongly agree that the healthcare system was not able to handle the pandemic and 68.6% believe that the government of Bangladesh needs support from the public to handle the pandemic [ 36 ]. In our study, 98.8% of the respondents were happy with the medical facilities provided by the government of Saudi Arabia, 93.8% were satisfied with the facility to send money, 90% were happy with the help provided by the embassy of India, and 87.7% were satisfy with the transport facility provided by government of India during the lockdown period. 5.11. Mental Health Status of the Migrants COVID-19 had not only influenced the economic and physical health of the people but also their mental status. This pandemic had drastically changed the mental status of the Indian migrant workers in Saudi Arabia. Table 9 describes the levels of anxiety, depression, and stress among the migrant workers. The majority of the migrants feel nervous (67.8%), depressed (63.3%), and lonely (72.2%) during the pandemic. It was also reported that 70% had difficulties in concentrating and 66.7% had a hard time in sleeping; however, the majority of them (91.7%) were feeling hopeful about the future, which shows silver lining at the end of the tunnel. It was also observed that only 2.2% of the migrants were Corona positive, 1.7% of their member households were Corona positive, and most of them (78.3%) were not scared of virus. Table 9. Mental health of the migrants during pandemic. Statements Variables Frequency (%) Have you felt nervous, anxious, or on edge? Yes 122 67.80% No 58 32.20% Have you felt depressed? Yes 114 63.30% No 66 36.70% Have you felt lonely? Yes 130 72.20% No 50 27.80% Have you felt hopeful about the future? Yes 165 91.70% No 15 8.30% I have a hard time sleeping because of the Corona Yes 120 66.70% No 60 33.30% I have had difficulties concentrating because of Corona Yes 126 70.00% No 54 30.00% Have you been tested Corona positive in Saudi Arabia? Yes 4 2.20% No 176 97.80% Are you scared of Corona virus? Yes 39 21.70% No 141 78.30% Does any of your family member infected of Corona virus? Yes 3 1.70% No 177 98.30% Source: Calculated by the authors from Google Forms questionnaire. 58 Healthcare 2021,9, 1152 5.12. Comparison of Mental Health of the Migrants by Age, Domicile and Education 5.12.1. Felt Nervous, Anxious, or on Edge Figure 3 describes feeling nervous, anxious, or on edge during pandemic by Indian migrants in Saudi Arabia. Around half of the young population aged between 20 to 40 felt nervous, and 78.5% of the migrants between the age of 40 to 50 and 90% of the age group above 50 felt nervous during the pandemic. The sample data reveal the fact that young migrants were less nervous than older migrants. The migrants from Bihar were found to be less nervous than Uttar Pradesh and other states of India. The nervousness of the migrants was also influenced by the levels of education of the migrants. Higher level of education implies a lower level of nervousness: 25% of doctorate, 59% of post-graduates, 64% of graduates reported nervousness during pandemic; however, 75% of intermediate, 79% of high school educated, and 61.5% of uneducated migrants reported nervousness. To investigate the relationship between nervous feeling by age, domicile, and education, correlation coefficient was applied. The result of correlation coefficient is shown in Table 10. Pearson’s correlation between felt nervous and age of the migrants was found to be negative and statistically significant (r = − 0.311, p< 0.01). Similarly, the relationship between felt nervous and domicile of the migrants was found to be positive and statistically significant (r = 0.262, p< 0.01). However, the relationship between felt nervous and education level of migrants was found to be positive but statistically insignificant (r = 0.133, p> 0.01). 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 20–30 30–40 40–50 50–60 UP Bihar Other NotEducated HighSchool Intermediate Graduation PostͲGraduation Doctorate Age Domicile Education Have you felt nervous, anxious, or on edge? Yes No Figure 3. Comparison of feeling nervous. 5.12.2. Felt Depressed Figure 4 represents the level of depression among the sample migrant workers during the pandemic. Less than half of the young migrants aged between 20 to 40 reported feeling depressed, however 72% of the migrants between the ages of 40 to 50 and 90% of the migrants aged above 50 reported feeling depressed during the pandemic. The sample data reveal that young aged migrants were less depressed than old age migrants. The 84.1% migrants from Uttar Pradesh reported feeling depression, however only 18.8% of the migrants from Bihar reported depression and 64.5% from other states were in depression during the pandemic. Lower levels of depression were reported by highly educated migrants (25% of doctorate, 54% of postgraduate, and 56% of graduate) and uneducated migrants (53%); however, 75% of the intermediate and high school educated migrants reported depression. To investigate the relationship between feelings of depression by age, domicile, and education, a correlation coefficient was applied. The result of the correlation coefficient is shown in Table 11. Pearson’s correlation between felt depressed 59 Healthcare 2021,9, 1152 and age of the migrants was found to be negative and statistically significant (r = − 0.368, p< 0.01 ). Similarly, the relationship between felt depressed and the domicile of the migrants was found to be positive and statistically significant (r = 0.236, p< 0.01). However, the relationship between felt depressed and education level of migrants was found to be positive but statistically insignificant (r = 0.140, p> 0.01). Table 10. Pearson’s correlation matrix. Statements Age p-Value Pearson Correlation Felt Nervous, Anxious, or on Edge −0.311 0.000 Felt Depressed −0.368 0.000 Felt Lonely −0.333 0.000 Hard time sleeping −0.372 0.000 Difficulties in concentration −0.315 0.000 Hopeful about the future −0.132 0.077 Statements Domicile p-Value Pearson correlation Felt Nervous, Anxious, or on Edge 0.262 0.000 Felt Depressed 0.236 0.001 Felt Lonely 0.178 0.017 Hard time sleeping 0.320 0.000 Difficulties in concentration 0.289 0.000 Hopeful about the future 0.165 0.027 Statements Education p-Value Pearson correlation Felt Nervous, Anxious, or on Edge 0.133 0.075 Felt Depressed 0.140 0.061 Felt Lonely 0.106 0.158 Hard time sleeping 0.175 0.019 Difficulties in concentration 0.151 0.044 Hopeful about the future 0.027 0.717 Statements Number of Family Member p-Value Pearson correlation Felt Nervous, Anxious, or on Edge −0.403 0.000 Felt Depressed −0.464 0.000 Felt Lonely −0.382 0.000 Hard time sleeping −0.446 0.000 Difficulties in concentration −0.429 0.000 Hopeful about the future −0.143 0.055 60 Healthcare 2021,9, 1152 ȱ 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 20–30 30–40 40–50 50–60 UP Bihar Other NotEducated HighSchool Intermediate Graduation PostͲGraduation Doctorate Age Domicile Education Haveyoufeltdepressed? Yes No Figure 4. Comparison of feeling depressed. Table 11. Comparison of psychological effect by number of family members. Statements Levels Number of Family Member Below 5 5 or More Have you felt nervous, anxious, or on edge? Yes 50.0% 77.5% No 50.0% 22.5% Have you felt depressed? Yes 35.9% 78.4% No 64.1% 21.6% Have you felt lonely? Yes 53.1% 82.8% No 46.9% 17.2% Have you felt hopeful about the future? Yes 87.5% 94.0% No 12.5% 6.0% I have a hard time sleeping because of the orona Yes 42.1% 80.2% No 57.9% 19.2% I have had difficulties concentrating because of corona Yes 50.0% 81.1% No 50.0% 18.9% 5.12.3. Felt Lonely Figure 5 shows the loneliness among the different types of the migrants. The migrants in the age group of below 40 reported less loneliness than the migrants above the age of 40. All migrants above the age of 50 reported that they felt lonely during the pandemic; however, 78.5% in the age group of 40 to 50, and around 56% of the age below 40 reported feeling loneliness during the pandemic. The migrants from Bihar were feeling less loneliness than the migrants from other states of India. The loneliness among differently educated migrants were not educated migrants (61.5%), high school (86.2%), intermediate (77.1%), graduation (67.2%), post-graduation (72.7%), and doctorate only 25%. To investigate the relationship between felt lonely by age, domicile and education, a correlation coefficient was applied. The result of the correlation coefficient is shown in Table 10. Pearson’s correlation between felt lonely and age of the migrants was found to be negative and statistically significant (r = − 0.333, p< 0.01). Similarly, the relationship between felt lonely and domicile of the migrants was found to be positive and statistically significant at the level of 0.05 (r = 0.178, 61 Healthcare 2021,9, 1152 p< 0.05). However, the relationship between felt lonely and education level of migrants was found to be positive but statistically insignificant (r = 0.106, p> 0.01). ȱ 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 20–30 30–40 40–50 50–60 UP Bihar Other NotEducated HighSchool Intermediate Graduation PostͲGraduation Doctorate Age Domicile Education Haveyoufeltlonely? Yes No Figure 5. Comparison of feeling lonely. 5.12.4. Felt Hopeful about the Future Figure 6 expresses the hopefulness about the future by migrant workers during the pandemic. The majority of all age groups of migrants, from all states and levels of education, reported hoping for better events to happen in the future; however, hopefulness in highly qualified migrants was reported to be less than in all other migrants. To investigate the relationship between felt hopeful about the future and age, domicile, and education, a correlation coefficient was applied. The result of correlation coefficient is shown in Table 10. Pearson’s correlation between felt hopeful about the future and age of the migrants was found to be negative but statistically insignificant (r = − 0.132, p> 0.05). Similarly, the relationship between felt hopeful about the future and domicile of the migrants was found to be positive and statistically significant at the level of 0.05 (r = 0.165, p< 0.05). However, the relationship between felt hopeful about the future and education level of migrants was found to be positive but statistically insignificant (r = 0.027, p> 0.05). 5.12.5. Difficulties in Sleeping and Concentration Figures 7 and 8 describe the anxiety among migrant workers through sleeping problems and difficulty in concentrating. The problem of anxiety was reported as more severe in the age group of above 40 than the age group below 40. It was also seen that migrants who belongs to Bihar were found to have less of an anxiety problem than the migrants from other states. The problems of anxiety were also correlated to the level of education of the migrants. More educated migrants reported less anxiety than the lower educated migrants during the pandemic. To investigate the relationship between difficulty sleeping and difficulties in concentration by age, domicile, and education, a correlation coefficient was applied. The result of the correlation coefficient is shown in Table 10. Pearson’s correlation between difficulty sleeping and age of the migrants was found to be negative and statistically significant (r = − 0.372, p< 0.01) and difficulties in concentration and age was also found to be negative and statistically significant (r = − 0.315, p< 0.01). Similarly, the relationship between difficulty sleeping and domicile of the migrants was found to be positive and statistically significant (r = 0.320, p< 0.01) and the relationship between difficulties in concentration and domicile was also found to be positive and statistically significant (r = 0.289, p< 0.01). The relationship between difficulty sleeping and education 62 Healthcare 2021,9, 1152 level of migrants was found to be positive but statistically significant at the level of 0.05. (r = 0.175, p< 0.05) and the relationship between difficulties in concentration and education level of the migrants was also found to be positive and statistically significant at the level of 0.05 (r = 0.151, p< 0.05). ȱ 0% 20% 40% 60% 80% 100% 20–30 30–40 40–50 50–60 UP Bihar Other NotEducated HighSchool Intermediate Graduation PostͲGraduation Doctorate Age Domicile Education Have you felt hopeful about the future? Yes No Figure 6. Comparison of feeling hopeful about future. ȱ 0% 20% 40% 60% 80% 100% 20–30 30–40 40–50 50–60 UP Bihar Other NotEducated HighSchool Intermediate Graduation PostͲ Graduation Doctorate Age Domicile Education I have a hard time sleeping Yes No Figure 7. Comparison of hard time sleeping. 5.12.6. Comparison of Mental Health Effect by Number of Family Members Table 11 presents the comparison of mental health impacted by number of family members of the migrants. Sample data disclose the fact that number of family members is directly related to the psychological stress on migrants. Only 50% of migrants with family members below 5 felt nervous, 35.9% depressed, 53.1% lonely, 42.1% had a hard time sleeping, and 50% had difficulties in concentrating; however, 77.5% of migrants with family members equal to 5 or above felt nervous, 78.4% depressed, 82.8% lonely, 80.2% have a hard time sleeping, and 81.1% had difficulties in concentration. The majority of the migrants (87.5% with family member below 5 and 94% with family member equal to 5 or above) felt hopeful about the future. The psychological problems are severe in the case of the migrants above the age of 40 and migrants with higher number of family members, 63 Healthcare 2021,9, 1152 because of social responsibility and low capabilities to face interpersonal challenges. To test the hypothesis and investigate the relationship between these statements and the number of family members, a correlation coefficient was applied. The result of correlation coefficient is shown in Table 9. Pearson’s correlation between these statements and the number of family members of the migrants was found to be negative and statistically significant. However, the relationship between felt hopeful about the future and number of family member is positive but statistically insignificant (r = −0.143, p> 0.05). ȱ 0% 20% 40% 60% 80% 100% 20–30 30–40 40–50 50–60 UP Bihar Other NotEducated HighSchool Intermediate Graduation PostͲ Graduation Doctorate Age Domicile Education I have had difficulties concentrating Yes No Figure 8. Comparison of having difficulties in concentration. 5.12.7. Comparing the Psychological Impact on Migrants before and during COVID-19 The results of the study confirm that 67.8% of the respondent migrants reported feeling nervous and 63.3% were depressed due to the COVID-19 pandemic. The level of psychological impact is much higher and severe among the migrant workers than before. One study was carried out to find out the prevalence of depression among migrant workers in AL-Qassim, Saudi Arabia, by taking a cross-sectional survey of 400 workers in 2016. The results of the study confirmed that 20% of the migrants reported the symptom of depression. It was also reported that level of depression varied by age but not by duration of stay [ 37 ]. Another similar study was conducted in 2011 to find out the prevalence of depression among migrant workers of UAE. The survey results of the 239 samples revealed that 25.1% of the migrants reported symptoms of depression. They also concluded that prevalence of depression is correlated with physical illness. In the same study, 6.3% of the respondents reported suicidal ideation and 2.5% had already attempted suicide [ 38 ]. Another study to find out the prevalence of depression among migrant workers was carried out in case of Qatar in 2016, which reported that 57.9% of the migrants had symptoms of depression [ 39 ]. Hence, it is clear that the percentage of migrants in our study which reported symptoms of depression and feeling nervous is much higher than the earlier studies; therefore, the COVID-19 pandemic had a severe psychological impact on Indian migrants working in Saudi Arabia. 6. Limitations The main limitations of the study are that it focuses on the Indian male migrants in Saudi Arabia with a sample data of 180, however the sample of female migrants are very less. It also uses few tests and strategies. The current study does not emphasis the effect on migrant’s family in India during pandemic. Apart from it, this study does not consider COVID-19 vaccination process, problems and its impact on migrants in Saudi Arabia. The future scope of study in this area needed to analyze the economic and psychological 64 Healthcare 2021,9, 1151 true in both ways, for those who get easily distracted or those who may find difficult to disconnect compromising their health and wellbeing [22]. On a different note, [ 18 ] refer that teleworking may also impair career progression because, when working remotely, workers are less on the radar for possible promotions. What is more, workers may feel less connected to the organization and miss the social contact and usual exchange with co-workers that can lead to fruitful collaborations [ 30 ]. In this regard, ref. [ 31 ] argues that personal interactions have a superior impact, particularly due to the enabled visual contact. Video calls and similar interactive devices fail to mimic this experience; hence, it is arguable that new technologies foster a particular type of distance among workers. 2.2. Teleworking during the Pandemic The effective implementation of teleworking as a means to mitigate the seemingly unavoidable economic impact of the COVID-19 was especially relevant to countries such as Portugal, in which positive signs of economic growth were appearing prior to the pandemic outbreak. Through covering at least the functions compatible with working at a distance, the benefits are evident since it allows workers to keep their jobs and allows firms to continue developing their activity, reducing the economic burden [31]. However, this measure was implemented without specific regulations, only based on a general agreement on teleworking of 2002, drawn on a different stage of ICT development and EU-based policies and directives regulating work, in general, and assuming by default that the same provisions would apply. Among these, are: EU Directive 2003/88/CE, about working time schedules; EU Directive 89/391/CEE on work health and hygiene; EU directive 2019/1158 about dealing with professional and familiar life and; EU Directive 2019/1152 on transparent and predictable work conditions [ 32 ]. The highlight goes to general rights, such as the voluntary nature of the work; respect for privacy; data protection; health and safety measures. Only in June 2020, an autonomous framework, aimed at informing a possible Europeanbased directive on digitalization, was put forth covering four specific areas: digital competence and job security; connection and disconnection modalities; artificial intelligence and human control; respect for human dignity and vigilance. The emphasis on these areas provides cues about the main concerns of conducting work activities with such dependence on ICT. Furthermore, a few recommendations are drawn so as to protect the workers’ rights on these conditions, starting with being informed about all the matters regarding equipment, working hours (normal and extraordinary), responsibilities, and costs. Other important provisions regard the costs being completely covered by the employer; the extraordinary hours reimburse; the right to sick leaves and, very importantly, an efficient and fair measurement and monitoring of working hours so as to protect workers from the risk of presenteeism. Besides not knowing the impacts and effects on a wide array of indicators in the long-run, either related to productivity and financial aspects, the individual and social coping to the hypothetical dissemination of teleworking is also uncertain. The literature puts forth two coping strategies: “integration” and “segmentation”/ “separation” [ 33 ]; both are based on how individuals redraw cultural boundaries around “work” and “home” when these overlap, as occurs in a teleworking format. These coping strategies, although generalist, provide a conceptual lens to the practicalities of accommodating the co-presence of these two settings with the ethical and values with which they are imbued [34]. In this regard, a separatist approach features the co-presence of “work” and “home” by adhering to strict temporal regimes as expressed in fixed office hours and closed-door spaces. Thus, symbolically as well as practically, “work” and “home” are kept apart. An integrative approach, on the other hand, tends to be more flexible and is likely to follow a more laissez-faire temporal regime, integrating domestic, personal activities (as physical exercise) and professional activities in common spaces [35]. 71 Healthcare 2021,9, 1151 Underpinning the coping strategies lies a fundamental element regarding the gendered division of household and childcare responsibilities [ 36 ]. Domestic inequalities are still a reality, particularly in countries with lower levels of gender equality and female empowerment [ 37 , 38 ] and, during the pandemic, they appear to have increased, especially amongst people with children [ 39 ]. More specifically, mothers reported a decrease in working hours and an increase in domestic and house care activities, as well as supervising children’s homework and didactic activities [ 39 , 40 ], with a negative impact on their wellbeing [ 36 ]. This is in line with the gendered expectations that remained the same and, despite the expansion of women’s roles in the last decade working outside the home, they are still expected to perform most of the domestic and care work [41]. What is more, gendered roles are prescriptive and proscriptive of attitudes and behaviour, and both have been evidenced, especially in the beginning of the pandemic, with women reporting more psychological distress and anxiety, and men reporting strength, more calm, and determination [42]. This forced experience on teleworking is perceived as an opportunity to catalyse “a wider adoption of teleworking practices also after the crisis” [ 42 ]. According to the European Foundation for the Improvement of Living and Working Conditions [ 43 ], more than three quarters of EU workers prefer to work from home, at least occasionally, even without COVID restrictions. Specifically, most EU workers indicate that they had a positive experience of teleworking and, albeit not exclusively, the most favoured option is to combine teleworking and on-site work. However, the overlapping of leisure and working time, domestic and labour routines, as well as the ICT intensive use are known to impact health and wellbeing. The negative effects are mostly psychological pressure, stress, vision problems, anxiety, headaches, fatigue, sleep disorders, and skeletal muscle functions [43]. In order to counteract the physical and mental health impact of telework, and to promote overall healthy behaviours during the pandemic, public health communication should not only focus on messaging information strictly regarding COVID-19 infection and its mitigation (e.g., prevalence, progression, death rate, mitigation measures) but also health promoting behaviours related to the management of in-door time and physical exercise. Indeed, some have advised for the maintenance of physical exercise during lockdown (e.g., [ 44 ]), and it has been argued to help reducing the negative health consequences of COVID-19 quarantine [45]. 2.3. Occupational Health in Telework: The Importance of Physical Activity According to the World Health Organisation [ 46 ], a healthy workforce is crucial for social and economic development. The WHO’s report on occupational health states that there is a continuous two-way interaction between individuals and the physical and psychological working environment, as the latter may affect, positively or negatively, the worker’s health, and productivity is, in turn, disturbed by the person’s well-being. In view of this, in order to ensure occupational health in telework in the context of COVID-19, it is important to underline the health risks and benefits associated with the sudden and largescale shift to telework, as well as the specific conditions that lead to better psychological and work outcomes [47]. Within the Portuguese context, a qualitative shift occurred in Health promotion initiatives, as evidenced in the official communication issued by The National Program for Physical Activity of the General Health Department (2020a) [ 42 ]. Aimed at counteracting the demanding restrictions, both resulting from spending more time at home collapsing routines and spaces as from being limited to enjoy public spaces, health authorities have been forceful in ensuing specific recommendations adapted to the circumstances. Very directive suggestions included: avoiding to seat or lie down for more than 30 min; reduce the time spent using technological devices; walk inside the house and conduct other physical activities; ‘invest in activities of cognitive stimulation (reading, puzzles); stretch and meditate as well as play with children [48,49]. 72 Healthcare 2021,9, 1151 This is backed up by WHO, suggesting 30 min of intense or moderated physical activity ([ 49 ]), particularly regarding older citizens [ 50 , 51 ], given their higher vulnerability to health problems and COVID-19. In this regard, aerobic home exercise has been advised, due to its fairly low complexity, low risk of injury, and high popularity [52]. Also, physical activity seems to be negatively correlated to cardiovascular disease and diabetes (e.g., [ 53 ]), which is especially noteworthy in the context of COVID-19, given that these constitute risk factors associated to respective severity and mortality (e.g., [ 53 ]). Additionally, exercise has been reported to positively impact anti-inflammatory response and reduce immunologic abnormality [54,55]. It is self-evident that physical activity has been impacted by the global efforts to mitigate the progression of COVID-19 infections [ 56 ]. In this social distancing phase, the type of physical activity should prioritize interiors or secure empty public spaces. Additionally, ref. [ 45 ] puts forth that people should practice physical exercise five to seven times a week, depending on the training intensity and modality (for example, if is resistance training it should be done two to three times a week, according to [57]. However, several obstacles may hinder the engagement of at-home physical exercise, namely the unavailability of training materials and equipment for moderate to intensive physical activity (particularly from those with a lower socio-economic level with less margin to acquire them), as well as difficulties in controlling training variables, such as adequacy of training exercises. Notwithstanding the obstacles, one may argue that the disruption of normal life and routines, allied to the sudden official Public Health communication issued by governments and reinforced by all media, led to a salience of physical activity in peoples’ minds. Even though physical activity promotion and healthier lives are two common claims in western societies, the pandemic added a tone of threat and urgency to it, either as a way to reinforce the overall physical health or to mitigate the psychological impact of the quarantine measures. In this regard, more fine-tuned research is needed to conclude the impact of the perception of public health messaging on the population ´ s adherence to governmental guidelines, including the appeal to physical exercise, as people tend to comply with governmental suggestions/orientations even when distrusting the government. This is particularly true in a time where information is not exclusively delivered directly by the institutions but rather mediated by both traditional and social media [ 58 ] with potential impact not only on compliance but also on mental health (e.g., [ 59 , 60 ]). In the context of COVID-19, studies suggest that using deontological moral advice when communicating public health advice (e.g., eliciting a sense of civic duty, ethical self-care) contributes to the engagement of behaviours that are helpful for health and wellbeing [61]. The ingrained notion of how important physical exercise is to physical and mental health found a more fertile ground because of the lack of parallel distractions. Digital landscapes (with emphasis of YouTube and social media) played a quintessential role in this dissemination, fuelling a wide variety of online training offers, thus, expanding the outreach of gymnasium, sport clubs, and personal trainers. Recorded and live sessions, mimicking physical training, push good practices and physical activity support further, often on a daily basis [ 62 ], with the common denominator of being mainly home-based. One may further argue that physical activity also contributes to mitigate the presenteeism and cognitive overload of connection, known to underpin physical and emotional exhaustion. In this regard, it is another aspect to take into account when drafting guidelines at EU level. Drawing on data collected during the first locked down, the present work contributes to unveil key elements that may be considered in communication and public policies regarding teleworking and physical activity tailored to reach different segmented groups of the populations. 73 Healthcare 2021,9, 1151 3. Materials and Methods 3.1. Participants and Procedure Data was collected from 14 March 2020 to 2 of May through an online survey in google forms which was shared via institutional and personal contacts. There were 1148 participants who replied, 69.9% women (n= 802) and 30.1% men (n= 346). The sample includes five different age groups: until 18 years old (n= 8; 0.7%); 18–24 years old (n= 277; 24.1%); 25–39 years old (n= 261; 22.7%); 40–59 years old (n= 466; 40.6%); above 60 years old ( n= 136 ; 11.2%). A substantial percentage of our sample has high education studies: nearly half is graduated at BSc level (n= 563; 49%), 19.8% at Master level (n= 227), and 7.1% has a PhD (n= 81). 15.9% (n= 182) has finished middle school and 8.3% (n= 95) completed 11 ◦ grade. More than half of the participants (n= 722, 62.9%) has a full-time job (40 h or more per week); 18.8% (n= 216) are students; 8.1% (n= 93) are retired; 4.4% (n= 51) work part-time jobs (16 to 30 h a week) and 44 (3.8%) are unemployed. Nearly half of the participants (n= 541; 47.2%) are married or living with a companion; 42.7% are single; 8.7% (n= 100) are divorced, and 16 (1.4%) are widowed. More than half (n= 589; 51.3%) have children. Approximately 60% of the participants indicate that their youngest child is still under age. 3.2. Questionnaire The questionnaire applied was made available online and included an informed consent describing the study, the aim and topics included and informing participants about the confidentiality of their answers. Only a positive reply would allow to proceed to other items related to topics out of the scope of the present article (factual knowledge, perceptions, attitudes, and behaviours towards the virus, its transmission, and consequences), socio-demographic information, and the following sections used in the present study (Supplementary Materials): Emotions: 5-point Likert scale items related to the emotional response felt in the last week (calm, nervous, sad, relaxed, and preoccupied). Teleworking and physical activity: 20 items related to teleworking (physical conditions, technological dimensions, and communication) and 17 items concerning online and physical activities. 4. Results 4.1. Adaptation to Teleworking The professional activities of most of the participants, 81.1% (n= 828), are compatible with teleworking, which is exclusively conducted from home. Interestingly, 34.2% consider that their professional routine has not changed, suggesting that there were sufficient elements in this period to maintain a perception of constancy. This may result from the fact that professional activities, nowadays, rely much more on online communication and technological media than on physically grounded activities. Hence, even though the context of work differed, the work process itself, at large, did not suffer significant changes. An aspect reported as being different was the time spent in work-related activities mediated by ICT. In this regard, 37.3% of the participants indicate spending more time online or using some ICT (e.g., computer; telephone); 26.4% indicate attending to more meetings and 34.6% to work for longer hours. Concerning the financial practicalities of this shift, 79.4% of the participants did not receive any reimbursement for extra expenses and 74.5% were not payed for extra hours. What is more, at the time, 18.8% did not even know if they would be reimbursed. The working hours and financial provisions appear to be at odds with the applicable European directives on this issue, regarding, in particular, the reimbursement for any extra costs related to teleworking and communications and the appropriate compensation for extraordinary working hours, particularly onerous for those participants who report working longer hours. Although these shortcomings may be understood in the light of the lack of national-based regulation on teleworking, they strengthen the need to reinforce 74 Healthcare 2021,9, 1151 public policy on this matter, at EU and national levels, as is currently ongoing based on the independent framework of digitalization rights (see SOC/660–EESC-2020-05278-00-00-ACTRA (EN) 2/18). As concerns, one of the key factors of teleworking—its physical space—among the surveyed, 72.2% (n= 594) were developing their activities in common and shared spaces, such as the living room (44.6%); the bedroom (19.6%) and the kitchen (4.5%). Only 27.8% had a specific room in the house dedicated solely to work without overlapping with other family dynamics, which is suggestive that the majority of our participants faced one of the most problematic issues in teleworking that is the physical blurred boundaries between work and home life [ 26 ]. This is even more impactful considering that 47.2% were in a relationship, 51.3% had children, of who 61% were under 18 and living at the house. Perceived as one potential disadvantage of teleworking [ 22 ] the shortcomings of the co-presence between work and home were particularly noteworthy in the context of COVID-19, given that, due to large-scale schools closing, parents not only have to juggle work and family life, but also manage children’s home schooling. Interestingly, in line with what was found in [ 36 ], the toll was felt heavier by the women. As shown in Table 1 below, when asked about the emotions felt in the past week, men clearly reported more positive emotions than women, including feelings of calm and relaxation, and, in contrast, women differed significantly from men in showing more negative emotions, including nervousness, sadness, and preoccupation. A one-way ANOVA (data not shown) shows that there are significant differences between the groups in all the emotions assessed. Table 1. Means and Standard deviation of emotions by gender. NMSD Calm Male 346 3.82 1.01 Female 802 3.25 1.04 Nervousness Male 346 2.25 1.10 Female 802 2.89 1.15 Sadness Male 346 2.60 1.16 Female 802 3.12 1.19 Relaxation Male 346 3.06 1.08 Female 802 2.53 1.04 Preocupation Male 346 3.28 1.11 Female 802 3.74 0.99 This strengthens the findings of [ 41 ] where women reported higher psychological distress whereas men were apparently calmer and stronger. These results may be influenced by the expected gendered display of emotions but also due to extrinsic pressures, since, in general, women were overall more burdened with more domestic and house care activities, as well as supervising children homework and didactic activities ([ 39 , 40 ]), with an expected negative impact on their wellbeing [36]. The analysis of the emotional reactions during this period also showed that, comparing all ages, participants above 60 years old are those that, albeit at a higher risk of pandemicrelated complications and more targeted by official communication, were feeling calmer (M = 3.54; DP = 1.06 ), more relaxed (M = 2.77; DP = 1.12), less preoccupied (M = 3.46; DP = 1.11 ) and less nervous (M = 2.40; DP = 1.12) than younger individuals. Sadness was the only emotion equally felt by all groups, appropriate to the loss and disruption felt at those times. The overall concern about older individuals’ health vulnerabilities and risk of social isolation and higher emotional impact [ 48 ] is not corroborated in our sample, with younger individuals feeling more negative emotions during these times. This may be related to the work-related uncertain processes and outcomes of the pandemic impact. Interestingly, students are the ones reporting higher levels of sadness (M = 3.12; DP = 1.13) whereas workers (62.9% of our participants have a full-time job and 4.4% a part time) report more 75 Healthcare 2021,9, 1151 nervousness and preoccupation, particularly part-time workers, the most psychologically distressed segment. Negative emotions in workers may also be aggravated by the fact that 40% of the participants work more hours than before at their work places. This result, besides not abiding by general regulations, is at odds with the more optimist view of teleworking as allowing workers to enjoy more free time for leisure [ 21 ] and is, in turn, in line with the risk of presenteeism [ 25 ] and overall negative impacts for the psychological well-being [27]. The work spillover during leisure hours is not, however, the only problematic issue. The non-verbal overload of digital interaction is known to not only fail at mimicking a healthier personal experience as to foster tiredness and irritability [ 31 ]. This is particularly evidenced in meeting platforms, such as Zoom or Microsoft Teams, in which increasing use is also corroborated in the present study. As shown in Figure 2 below, Zoom was the more frequent new ICT platform followed by Microsoft Teams. The remaining were already commonly used for communicating with teams and co-workers, especially e-mail (99.9% of the participants), followed by WhatsApp (56.4%) and Messenger (40.8%). Other studies have reported similar results, in which the use of and dependence upon social media platforms, such as Zoom, Microsoft Teams, and WhatsApp, to stay connected for work, education, and social purposes, have seen an exponential growth in users during that time (e.g., [63,64]) Figure 2. New ICT tools. In this regard, 64.6% of our participants report not using ICT for leisure, suggesting their use as working or utilitarian tools. One of these utilitarian aims, besides work, is online shopping, with 45.8% of the participants reporting it as a common practice. For 23.1%, the frequency of on-line purchasing has increased during the pandemic that also brought a different choice of products (depicted in Table 2). Expectedly, considering the measures of social isolation and quarantine at place, there was a substantial increase in the acquisition of essential goods and foodstuffs. Gadgets and technology purchase also increased, probably due to the higher ICT use during these times for work and entertainment purposes. Interestingly, there was a fall in all of the other products, particularly clothes. Table 2. Online purchases before and during the pandemic. Before During Pandemic Essential goods and foodstuffs 26.9% 46.8% Clothes 46.5% 9.4% Cosmetics 5.4% 4.7% Books 15.8% 7.4% Gadgets/Technology 5.4% 31.7% Among the 35.3% who actually use ICT for leisure, the interests and focuses are varied (see Table 3). Physical exercise classes and apps are the more frequent on-line based activities, and this interest and actual investment speaks favourably about the widespread 76 Healthcare 2021,9, 1151 dissemination of the importance of physical exercise. This in-home practice even surpassed the search for entertainment-based activities, as internet searches, movies and TV shows, and games. Table 3. Categories of on-line activities for leisure. N% Physical exercise classes and apps 107 27.30% Internet searches (sites, YouTube) 103 26.40% Movies and tv shows (Netflix, HBO) 98 25.10% Games 71 18.20% Cultural activities (cinema, theater, concerts) 50 12.80% Social Media 35 8.90% 4.2. Physical Activity The interest in being physically active is not only evidenced by searching and purchasing related physical activity apps and classes online, but also by the fact that 70.1% of the participants were already active before the pandemics, 53.1% practicing a specific sport and 46.9% recreative and leisure physical activities. Even though 54.1% report that the physical activity decreased with the pandemic, 27.7% were still practicing up to 3 times, 19.9% once a week, and 17.3% up to seven days a week, which is not so far from the optimal practice suggested in [ 45 , 57 ]. These regular habits are even more important considering that 54.2% of our participants work seated at the computer with the potential sedentarism and collateral psychological pressure, stress, vision problems, anxiety, headaches, fatigue, sleep disorders, and skeletal muscle functions [ 57 ]. Furthermore, there was a substantial decrease for younger participants (52%) and for participants above 60 years old (66%), which strengthens, even more, the governmental concerns in targeting this age in particular [50]. As expected, there was a shift in the place of physical practice and whereas 91.7% of these activities were practiced outside the house with the pandemics, only 20.2% of the participants were able to keep that routine. Moreover, 79.8% of the participants report to conduct their physical activities inside the house, suggesting an adherence to the message issued by governments and reinforced by all media concerning the practice of physical activity [ 56 ]; ICTs, in particular, digital landscapes such as YouTube, social media, and sites, appear to be of nuclear importance in the adoption of this practice mimicking a real life context of physical practice and connection [ 62 ] while 39.3% of the participants report following a regular routine nowadays. Another evidence of the compliance of governmental indications is the difference between the role of group-based activities of physical exercise before (41.6%) and during the pandemic (3.9%). There was no change, however, in the percentage of participants exercising in the company of one more person. Despite the overall frequency decrease, one may argue that what changed for most of them was the adjustment to different routines since—up to a higher or lesser degree—they have started to practice inside the house and, more often, alone (73.1% of the participants in contrast with 33.8% prior to the pandemic). The practice of physical exercise appears to be more frequent in participants with a master degree (81%) and a PhD (79%) and the least adopted by those with a compulsory education (55.8%). These results follow the widely acknowledged association of physical exercise with health behaviour and better health in general [ 65 ] being perceived by some authors as the single most important and constant influence in health preservation [66]. On one hand, it is argued that formal education fosters knowledge and values related with seeking and comprehending health-related information as well as acting upon it. By contrast, lower educated people are at higher risk of not engaging in the desirable levels of physical activity [ 67 ], which can also be linked to more material problems (such as housing general conditions and available space) or poor health experienced by older lower educated people. 77 Healthcare 2021,9, 1151 Accordingly, public health communication should emphasize beneficial and low complexity exercises (as aerobic home) assessable to all segments of the population. 5. Conclusions 5.1. Implications The COVID-19 pandemic has embodied a major challenge, not only for the health system, but also for services, firms, workers, and employers, due to the upswing suddenly experienced by remote working technologies. The spread of teleworking and the use of technological platforms, in this context, has been considered essential to keep social distancing in workplaces and between employees and users/clients. Given the speed of change in result of political measures, services, and companies had very little time to put together a work at distance plan. Even though the COVID-19 pandemic and its mitigation methods have noticed, these past months, a gradual decrease in a number of countries concerning social distance, the extensive use of teleworking is expected to continue. As recently stated by the European Parliament Committee on Employment and Social Affairs, “the extensive use of telework poses a number of challenges and requires a re-think of the way work is performed, coordinated, and regulated” ([ 68 ], p. 14), bearing in mind its positive and negative impacts. On this, several hazards to the health of teleworkers have been highlighted in literature (see inter alia [ 69 ]), namely physical (e.g., awkward postures, repetitive movements, and long periods of continuous work, increased rate of physical inactivity, and sedentarism) and psychosocial (e.g., sleeping disorders, work-related stress, and social isolation) ones. If COVID-19 events have transformed the working conditions and modified the employer-worker-user/client relationships, making telework unlikely to return to prepandemic levels, it is essential that policymakers, services, and firms realize the challenges associated with this phenomenon, building knowledge to provide the basis for change, improvement, and, accordingly, promote generative learning from research. This study, conducted within the COVID-19 crisis context in Portugal, intended to grasp specificities of the adaptation to the lock down and social distancing measures, in what concerns specifically teleworking conditions and physical activity practice. From this study, it is possible to derive some findings with potential implications for the immediate and post-pandemic settings. First, the workload and time spent in teleworking were higher than in the physical format, i.e., before the pandemic. Besides confirming the risk of presenteeism (foreseen as disadvantage of this format) it reinforces the need to draft clear and encompassing regulations and policies protecting the workers from this probable spill over. Our results also unveiled a problem related to the workers’ personal sphere, that is, the lack of a specific space at home exclusively for work. The overlapping of spaces and blurred boundaries between work and home life is known to cause entanglement and confusion as well as be much more demanding in self-discipline and time management. Even though it is harder to tackle this issue from a public policy viewpoint, it may be mitigated at an organizational level: team-leaders and employees need to be briefed and prepared in the most co-constructive ways to conduct work in these different and heterogeneous conditions. Under a common teleworking policy, trainings, specific performance criteria and weekly check-ins to gauge their experience and address any concerns should be adopted. A peopleoriented mind set would be beneficial, acknowledging and managing, as much as possible, the pressing anxiety and stress that may result from these conditions. On the other hand, this research also suggests that women were subjected to more emotional stress and impact on psychological wellbeing. This requires a tailored approach to raise awareness about expected gendered biases while empowering women to assert and define a more balanced distribution. Given that this is a structural societal issue, it may be more effective if put forth and advocated by public or organizational policies. The same concerns apply to the wide use of ICT, also corroborated here, known to induce a cognitive overload with a negative impact on wellbeing and physical health. 78 Healthcare 2021,9, 1151 Efforts in tailoring occupational health programs and training should be put in motion and enforced by public policies. This may also include the emphasis, already noticeable, of the perks and necessity of physical activity, no longer seen as a hobby but as a complementary part of a work routine. This study indicates that, despite the difficult conditions and adverse times, there was an effort to continue to practice physical activities (also evidenced in the search for related online classes and apps), which speaks favourably of the receptivity to Health communication and individual predispositions. In addition, the lack of reimbursement for extra work time or equipment, at par with the workers’ unawareness of their rights and what they are entitled to, is indicative of the urgency in drafting regulations and legislations at the European and national level specifically covering telework. Considering the gradual shift towards flexible work practices, these regulations should be well-known by the workers. 5.2. Limitations of the Study and Future Research Lines The present study has two main limitations to be taken into account and frame the results interpretation. The first concerns its exploratory and descriptive nature, reflected both in the questionnaire design and in the analyses conducted which targeted only a description of general conditions and particular behaviours and practices of the participants. The second regards the non-probabilistic sampling method through institutional and personal contacts, which resulted in an over-sampling of highly educated individuals. In this regard, the results must be considered in the light of this particular WEIRD sample and national context. Notwithstanding, considering the increasing role teleworking is playing in society, this study highlights some patterns that may inform further research and policy design particularly in the analysed context, worth to emphasize that public policies and cooperation among social partners are crucial to ensure that new, efficient, and welfareimproving working methods emerging during the crisis are maintained and developed once physical distancing is over. To maximize productivity and welfare gains inherent in the use of more widespread telework, governments should promote investments in the physical and managerial capacity of firms and workers to telework and address potential concerns for the workers’ health, well-being, and longer-term innovation related, in particular, to the excessive downscaling of workspaces. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/healthcare9091151/s1, Questionnaire. Author Contributions: Conceptualization, T.F. and G.S.; methodology, T.F. and G.S.; formal analysis, T.F. and G.S.; writing—T.F.; G.S. and S.A.C.; proofreading—T.F.; G.S. and S.A.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Ethical review and approval were waived for this study due to the absence of risk in data collection or sensible information accessed. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Acknowledgments: This work was partly financially supported by the research unit on Governance, Competitiveness and Public Policy (UIDB/04058/2020) + (UIDP/04058/2020), funded by national funds through FCT–FundaçãoparaaCiência e a Tecnologia. Conflicts of Interest: The authors declare no conflict of interest. 79 Healthcare 2021,9, 1151 References 1. Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020,323, 1061–1069. [CrossRef] [PubMed] 2. Bedford, J.; Enria, D.; Giesecke, J.; Heymann, D.L.; Ihekweazu, C.; Kobinger, G.; Ungchusak, K. COVID-19: Towards controlling of a pandemic. Lancet 2020,395, 1015–1018. [CrossRef] 3. Barr, K. Coronavirus: What Is Herd Immunity and Is It a Possibility for the UK? Independent. 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Standen, P.; Daniels, K.; Lamond, D. The home as a workplace: Work–family interaction and psychological well-being in telework. J. Occup. Health Psychol. 1999,4, 368–381. [CrossRef] [PubMed] 80 Healthcare 2021,9, 846 Table 1. Documents of employment promotion policy for college graduates. No. Policy Document Issuing No. Issuing Department Issuing Date P1 Notice on Implementing Employment Work during the Period of Epidemic Prevention and Control Tianjin People’s Office issued (2020) No. 29 Ministry of Human Resources and Social Security of China/Ministry of Education of China/Ministry of Finance of China/Ministry of Transportation of China/National Health Commission 2020.2.5 P2 Notice on Carrying out the National Online Joint Recruitment of 2020 College Graduates-24365 Campus Recruitment Service Activities Ministry of Education of China (2020) No. 2 Office of the Ministry of Education of China 2020.2.28 P3 Notice on Carrying out Employment and Entrepreneurship of the 2020 National College Graduates during COVID-19 Ministry of Education of China (2020) No. 2 Ministry of Education of China 2020.3.4 P4 Notice on Carrying out Public Recruitment of College Graduates by Public Institutions during COVID-19 Human Resources and Social Security of China (2020) No. 27 General Office of the Organization Department of the CPC Central Committee of China/General Office of the Ministry of Human Resources and Social Security of China 2020.3.11 P5 Suggestions on Strengthening and Stabilizing Employment during COVID-19 Office of the State Council of China (2020) No. 6 Office of the State Council of China 2020.3.18 P6 Notice on Implementing Some Vocational Qualifications “First Employed then Passed the Exam” Human Resources and Social Security of China (2020) No. 24 Ministry of Human Resources and Social Security of China/Ministry of Education of China/Ministry of Justice of China/Ministry of Agriculture and Rural Affairs of China/Ministry of Culture and Tourism of China/National Health Commission of China/National Intellectual Property Office of China 2020.4.21 P7 Notice on Holding the 2020 National College Graduate Employment Network Alliance Recruitment Week Ministry of Education of China (2020) No. 7 Ministry of Education of the People’s Republic of China 2020.4.23 P8Notice on Carrying out the Pioneer base for Entrepreneurship and Employment National Development and Reform Commission of China (2020) No. 310 General Office of the National Development and Reform Commission of China/General Office of the State-owned Assets Supervision and Administration Commission of the Ministry of Education of China/General Office of the Ministry of Human Resources and Social Security of China 2020.4.24 P9 Notice on National SME Online Recruitment of College Graduates in 100 Days Ministry of Industry and Information Technology of China (2020) No. 179 Provincial Department of Industry and Information Technology of China/Provincial Department of Education of China/Provincial Department of Human Resources and Social Security of China 2020.4.27 P10 “Notice on Public Recruitment of Kindergarten Teachers in Primary and Secondary Schools in 2020 Human Resources and Social Security of China (2020) No. 28 Ministry of Human Resources and Social Security of China/Ministry of Education of China/Central Planning Office of China/Ministry of Finance of China 2020.5.9 87 Healthcare 2021,9, 846 Table 1. Cont. No. Policy Document Issuing No. Issuing Department Issuing Date P11 Notice on Implementation of the “Three Supports and One Support” Plan for College Graduates in 2020 Human Resources and Social Security of China (2020) No. 57 General Office of the Ministry of Human Resources and Social Security of China/General Office of the Ministry of Finance of China 2020.5.19 P12 Notice on Encouraging Scientific Research Projects to Absorb College Graduates Ministry of Science and Technology of China (2020) No. 132 Ministry of Science and Technology of China/Ministry of Education of China/Ministry of Human Resources and Social Security of China/Ministry of Finance of China/Chinese Academy of Sciences/Natural Science Foundation of China 2020.5.27 P13 Notice on Further Development of Research Assistant Positions in Colleges and Universities to Absorb Graduate Employment Ministry of Education of China (2020) No. 23 Office of the Ministry of Education of China 2020.6.4 P14 Notice on Guiding and Encouraging College Graduates to Work and Start Business in Urban and Rural Communities Human Resources and Social Security of China (2020) No. 53 Organization Department of the Party Committee of each city (prefecture)/Civilization Office of China/Civil Affairs Bureau of China/Education Administrative Department of China/Finance Bureau of China/Human Resources and Social Security Bureau of China/Health and Health Committee of China 2020.6.22 P15 Notice on Precise Assistance for Employment of College Graduates from Poor Families in 52 Poverty Counties” Ministry of Education of China (2020) No. 21 General Office of the Ministry of Education of China/General Office of the Ministry of Human Resources and Social Security of China/General Department of the Poverty Alleviation Office of the State Council of China 2020.7.2 P16 Suggestions on Allowing Medical College Graduates to Exempt from Examination to Apply for Practicing Registration of Rural Doctors National Health Commission of China (2020) No. 11 National Health Commission of China 2020.7.6 Based on policy text, this paper uses ROST CM [ 22 , 23 ] software to preprocess the policy text, such as word segmentation and keyword frequency statistics, in order to extract the key content from the policy document. The specific process is as follows: first, the policy text is segmented, then the word frequency of the document after word segmentation is ranked, and finally the word segmentation results are sorted according to the word frequency from high to low. The results are shown in Table 2. In addition, the Ucient software was used to build a co-occurrence network for the documents after word segmentation, and the results are shown in Figure 2. Each node in the network represents a keyword, and if there is a line between nodes, the keywords have a symbiotic relationship. At the same time, nodes are displayed according to the keyword centrality. If the keyword has higher centrality, the keyword frequently appears together with other keywords in the network [24]. 88 Healthcare 2021,9, 846 Table 2. Statistics of keyword frequency in employment promotion policy documents for college graduates. Keyword Frequency Keyword Frequency employment 1328 resource 678 graduate 1322 safeguard 676 college 1314 implement 575 recruitment 1166 society 474 service 1130 scientific research 371 company 1120 program 271 entrepreneurship 1111 strengthen 168 position 890 epidemic 166 organization 887 personnel 166 enterprise 882 policy 164 department 781 grassroots 88 Figure 2. Co-citation networks of keyword in employment promotion policy documents for college graduates. It can be seen from the keyword frequency distribution and keyword co-citation networks of the aforementioned policy documents that “employment”, “service”, and “position” rank first among the high-frequency words. This is different from the previous situation for college graduates. The COVID-19 has led universities and companies to cancel offline job fairs for the class of 2020, which are the main job opportunities for fresh graduates. Therefore, during COVID-19, the most important thing for the government is to mobilize all units to implement online employment services, expand employment channels and increase employment opportunities. From the two high-frequency words “entrepreneurship” and “grass-roots level”, we can see that in order to increase the employment opportunities of college graduates, the government has repeatedly mentioned encouraging, supporting and guiding graduates to find jobs at the grass-roots level, stabilizing the environment for innovation and entrepreneurship, and giving full play to the important role of “mass entrepreneurship and innovation” in supporting employment. 4.2. Evaluating Employment Promotion Policy Documents for College Graduates Based on PMC Model At present, the more advanced international policy text evaluation method is the PMC Index Evaluation Model established by Estrada [ 25 ]. This model believes that ev89 Healthcare 2021,9, 846 erything is constantly in motion and interconnected, so any relevant variable cannot be ignored. Its innovation is that it uses binarydigits 0 and 1 to balance all variables and emphasizes that the number and weight of variables should not be limited, so that the advantages and disadvantages and internal consistency of a policy can be analyzed from various dimensions [26]. Most existing policy evaluation methods have problems such as strong subjectivity and low accuracy. However, the PMC index model method can largely avoid subjectivity and improve accuracy because it obtains raw data through text mining. In addition, the effectiveness of the PMC model has been verified in the literature [ 25 ]. In the policy analysis in this paper, the PMC index model takes variables into extensive consideration, which not only can comprehensively analyze the merits and demerits of a policy, but also has the advantages of index traceability and grade identification, and scientifically quantifies the consistency level of each policy from different dimensions. Therefore, this paper introduces the PMC index model to quantitatively evaluate the employment promotion policy for college graduates under COVID-19 and obtains the key points of the policy content from the outstanding policy documents with a PMC index score of 9–10. Generally, the establishment of a PMC index model includes the following steps: (1) establishing a PMC index evaluation index system, (2) establishing a multi-input-output table, and (3) calculating twolevel variable values and PMC index. 4.2.1. Classifying the Variables and Setting Parameters of PMC Index Model Referring to Estrada and the existing literatures [ 27 – 29 ] and combining with the specific characteristics of college graduates’ employment promotion policy, this paper establishes 10 first-level variables and 66 s-level variables. The results are shown in Table 3. Table 3. PMC evaluation variables of employment promotion policy for graduates. First-Level Variables Second-Level Variables No. Second-Level Variables Name Second-Level Variables No. Second-Level Variables Name Nature of X1policy X1:1 supervision X1:2 support X1:3 advisement X1:4 encourage X1:5 guide Time of X2policy X2:1 transition period X2:2 short term X2:3 this year Field of X3policy X3:1 economy X3:2 public management X3:3 talent X3:4 social security X3:5 technology X3:6 institution Function of X4policy X4:1 expand demand X4:2 normative guidance X4:3 strengthen protection X4:4 institutional constraints X4:5 optimize system Objective of X5policy X5:1 enterprise X5:2 college graduates X5:3 college X5:4 all provinces, cities, autonomous regions, and municipalities directly under the Central Government X5:5 directly subordinate agency X5:6 key areas of the epidemic X5:7 ministries and commissions of the State Council Content of X6policy X6:1 resumption of work and production X6:2 employment subsidy X6:3 employment service X6:4 encourage employment and entrepreneurship X6:5 stable employment X6:6 broaden employment channels X6:7 strengthen training X6:8 encourage grassroots work X6:9 accurate employment assistance 90 Healthcare 2021,9, 846 Table 3. Cont. First-Level Variables Second-Level Variables No. Second-Level Variables Name Second-Level Variables No. Second-Level Variables Name Issuing agency of X7 policy X7:1 Ministry of Human Resources and Social Security X7:2 Ministry of Education X7:3 Ministry of Finance X7:4 Transportation Department X7:5 National Health Commission X7:6 provinces and cities X7:7 Local and subordinate colleges and universities X7:8 General Office of the Central Organization Department X7:9 Department of Justice (Bureau) X7:10 Department of Agriculture and Rural Affairs (Agriculture, Animal Husbandry and Veterinary Medicine, Fishery) (Bureau, Commission) X7:11 Department of Culture and Tourism (Bureau) X7:12 Intellectual Property Office (Intellectual Property Management Department) X7:13 SASAC Incentives of X8policy X8:1 employment subsidy X8:2 job creation X8:3 tax incentives X8:4 talent incentive X8:5 online employment X8:6 multi-channel employment X8:7 incentives for primary services X8:8 self-employed X8:9 skills Training X8:10 employment guidance service X8:11 encourage teaching X8:12 employment assistance X8:13 lower the barriers to employment Evaluation of X9policy X9:1 clear objective X9:2 feasible plan X9:3 sufficient reference X9:4 detailed planning X9:5 encourage employment Publication of X10 policy — The weights of the second-level variables in Table 3 are set to the same value, and all the parameter values of the second-level variables are set to binarydigits 0 and 1. If the content of the policy document involves the meaning of the second-level variables, it is assigned the value 1; otherwise, it is 0. 4.2.2. Constructing Input-Output Table The input-output table is a data analysis framework that can store a large amount of data and use multidimensional measurement of a single variable. It is composed of numerous first-level variables and second-level variables that are not restricted by variables. The first-level variables have no fixed order and are independent of each other, and the weights of the second-level variables are equal [30], as shown in Table 4. The second-level variables’ values are assigned according to the keywords obtained in Section 4.1. When the policy text data contains the keywords corresponding to the second-level variables, the value is assigned to 1; otherwise, it is 0. Compared with the subjectivity of expert scoring, this method is more objective and scientific. 91 Healthcare 2021,9, 846 Table 4. Input-output table. First-Level Variables Second-Level Variables X1X1:1 X1:2 X1:3 X1:4 X1:5 X2X2:1 X2:2 X2:3 X3X3:1 X3:2 X3:3 X3:4 X3:5 X4X4:1 X4:2 X4:3 X4:4 X4:5 X5X5:1 X5:2 X5:3 X5:4 X5:5 X5:6 X5:7 X6X6:1 X6:2 X6:3 X6:4 X6:5 X6:6 X6:7 X6:8 X6:9 X7X7:1 X7:2 X7:3 X7:4 X7:5 X7:6 X7:7 X7:8 X7:9 X7:10 X7:11 X7:12 X7:13 X8X8:1 X8:2 X8:3 X8:4 X8:5 X8:6 X8:7 X8:8 X8:9 X8:10 X8:11 X8:12 X8:13 X9X9:1 X9:2 X9:3 X9:4 X9:5 X10 — 4.2.3. Calculating PMC Index The PMC index of the policy documents in Table 1 is calculated below. The calculation method is as follows: Xi:j∼n, (1) iis first-level variables; jis second-level variables, i,j= 1,2,3,4,5. . . . ∞. Xi=∑n j=1 Xi:j n, (2) nis the amount of second-level variables, n= 1,2,3,4,5. . . . ∞. PMC =X1(∑5 a=1X1:a 5)+X2(∑3 b=1X2:a 3)+X3(∑5 c=1X3:c 5)+X4(∑5 d=1X4:d 5) +X5(∑7 e=1X5:e 7)+X6(∑9 f=1 X6:f 5)+X7(∑13 g=1 X7:g 5)+X8(∑13 h=1X8:h 5) +X9(∑5 k=1X9:k 5)+X10 (3) First, determine the value of the second-level variable X i:j according to Formula (1), then calculate the value of each first-level variable according to Formula (2), and finally bring each first-level variable into Formula (3) to calculate the PMC index of different policies. The PMC index evaluation criteria can be obtained from the literature [ 21 ]: 9–10 points (perfect level), 7–8.99 points (excellent level), 5–6.99 points (acceptable level), 0–4.99 points (bad level). This method obtains the ranking and rating of the PMC index of the employment promotion policy for college graduates, and the results are shown in Table 5. Table 5. PMC index of employment promotion policy documents for college graduates. X1X2X3X4X5X6X7X8X9X10 PMC Index Ranking Depression Index Rating P11 0.33 0.5 0.4 0.71 0.56 0.38 0.23 0.4 1 5.51 5 4.49 acceptable P20.4 0.67 0.33 0.4 0.43 0.22 0.23 0.08 1 1 4.76 9 5.24 bad P31 0.67 0.67 1 0.43 0.78 0.15 0.69 1 1 7.39 2 2.61 perfect P40.8 0.67 0.33 0.6 0.43 0.56 0.23 0.31 0.6 1 5.53 4 4.47 acceptable P51 1 1 0.8 0.86 0.89 0.15 0.69 0.6 1 7.99 1 2.01 perfect P60.4 0.67 0.5 0.4 0.29 0.22 0.54 0.23 0.8 1 5.05 8 4.95 acceptable P70.2 0.33 0.33 0.2 0.43 0.22 0.15 0.15 0.6 1 3.61 15 6.39 bad P80.6 0.33 0.33 0.8 0.71 0.44 0.23 0.46 0.8 1 5.7 3 4.3 acceptable P90.4 0.33 0.17 0.4 0.29 0.33 0.08 0.08 1 1 4.08 14 5.92 bad P10 0.4 0.33 0.5 0.4 0.29 0.33 0.31 0.23 0.8 1 4.59 11 5.41 bad P11 0.8 0.33 0.5 0.4 0.29 0.33 0.23 0.23 1 1 5.11 7 4.89 acceptable P12 0.6 0.33 0.33 0.4 0.29 0.22 0.31 0.15 1 1 4.63 10 5.37 bad P13 0.6 0.33 0.33 0.4 0.29 0.22 0.31 0.15 0.6 1 4.23 12 5.77 bad P14 0.6 0 1 0.8 0.29 0.22 0.38 0.38 0.6 1 5.27 6 4.73 acceptable P15 0.4 0 0.17 0.4 0.43 0.44 0.23 0.23 0.8 1 4.1 13 5.9 bad P16 0.6 0 0.17 0.4 0.43 0.22 0.08 0.15 0.2 1 3.25 16 6.75 bad average 0.61 0.39 0.45 0.51 0.43 0.39 0.25 0.28 0.74 1 92 Healthcare 2021,9, 846 From the evaluation results in Table 5, it can be seen that among the 16 college graduate employment promotion policies, eight of the policy evaluation results are acceptablelevel or above, accounting for 50%, among which two are perfect, and the policy content contained in the perfect policy document is more comprehensive. The target audience is wider, and the steps involved in the implementation measures are more detailed. Therefore, in order to extract the key points in the policy documents, the contents of the P 3 and P 5 perfect-level policy documents are selected and summarized. Due to the diversity of the measures proposed in the policy and their different focuses, the content of the policy is divided into four areas: (1) Increase the opportunities for further education and reduce the number of fresh graduates who are in urgent need of employment. (2) Broaden employment information circulation channels, and guide universities and colleges to carry out extensive online employment. (3) Provide employment subsidies, lower employment restrictions, alleviate employment anxiety of recent graduates, and improve employment benefits of recent graduates. (4) Increase position and increase labor demand. According to the above four aspects, the employment promotion policy measures for college graduates can be divided into four categories: channel measures, transference measures, subsidy measures, and position measures. 5. Analyzing Social Effects on Employment Promotion Policies for College Graduates As a reflection of public sentiment and public opinion, online public opinion not only manifests its influence on major developments, but also penetrates into the political level, becoming an important channel for the government to listen to and understand public opinion. In order to dig out the public’s attitude and response to the official employment promotion policy under the COVID-19 pandemic, the corresponding topic comment information on the Weibo is crawled, and social implementation effect of employment promotion policy is analyzed based on the comments. 5.1. Acquiring and Preprocessing Data 5.1.1. Acquiring Data This paper searches related topics for 4 kinds of measures on Weibo and selects the 15 topics discussedmost frequently as the data crawling objects. Each topic is shown in Table 6 . Table 6. Related Weibo topics. Policy Weibo Topic Channel measures 24.356 all-day online campus employment service Encourage multiple methods such as webcasting Single assistance between domestic colleges and Hubei colleges Transference measures Enrollment of postgraduate students increased by 189,000 Expand the scale of enrollment for postgraduates and undergraduates Expand the postgraduate enrollment of retired soldiers in college Subsidy measures Provide employment subsidies for college graduates in many places The highest award for innovation and entrepreneurship of Tianjin college graduates is 300,000 RMB Find a job within two years and go through the employment procedures according to the current term Graduates can keep their household registration files in the school for two years Position measures State-owned enterprises expand the enrollment of college graduates this year and next two years Expand the recruitment of primary and secondary school teachers Implement “first recruited, then passed the exam” Special post teachers plan to increase recruitment by 5000 Develop research assistant position to attract college graduates 93 Healthcare 2021,9, 846 This paper trawls the related Weibo comments on 15 topics. The trawled content includes the publisher ID, the content of the comment, comment time, commenter ID, the number of followers, the number of subscribers, and Weibo number. This paper uses python to obtain a total of 65,487 posts, including 9596 posts for channel measures topics, 17,003 posts for transference measures topics, 7671 posts for subsidy measures topics, and 28,849 posts for position measures topics. The data format is shown in Figure 3. Figure 3. Data format. 5.1.2. Data Cleaning Because invalid data and incorrect data inevitably appear in the trawling process, these data are rarely utilized in the analysis process or cause large errors in the results, thusthey need to be deleted. Data cleaning mainly deletes repeatedly collected data, repeated expression words, shorter sentences, meaningless, or unclear sentences. After data cleaning, a total of 61,311 valid posts were obtained. 5.1.3. Word Segmentation and Word Frequency Statistics As the content of the comments are all in Chinese, the Jieba Chinese word segmentation package [ 31 ] is used to perform word segmentation on the Weibo comments in the Python environment and remove stop words that cannot represent text characteristics. Because the research object of this paper is the employment policy for college graduates under COVID-19 pandemic, the nouns that appear frequently in the document after word segmentation are “student, society, employment”, etc., such words are more neutral and have less meaning for word frequency analysis. Therefore, this type of word is also added to the stop word dictionary. On this basis, the top 100 effective high-frequency words are sorted out as follows: “teacher”, “quota”, “postgraduate”, “epidemic”, “fresh graduate”, “fractional line”, “condition”, “file”, “previous graduate”, “full-time”, “employment rate”, “labor force”, “talent”, “young people”, “master”, “quality”, “civil servant”, “parttime”, “housing price”, “Wuhan”, “workload”, “research assistant”, “proportion”, “normal major”, “doctor”, “Guangdong”, “qualification”, “written examination”, “special post teacher”, “Chongqing”, “student source”, “mathematics”, “college promotion”, “unit”, “age”, “college”, “threshold”, “preliminary examination”, “junior college student”, “origin”, “Sichuan”, “energy”, “household registration”, “re-examination”, “unemployment rate”, “poor student”, “welfare”, “enterprise”, “area”, “level”, “accomplishment”, “bachelor”, “doctoral student”, “Shandong”, “Henan”, “junior college”, “registered residence”, “treatment”, “Beijing”, “elementary school”, “salary”, “subsidy”, “university”, “head teacher”, “interview”, “tripartite agreement”, “vocational school”, “contract”, “rural area”, “preschool education”, “ability”, “Anhui”, “township”, “undergraduate”, “Shanghai”, “region”, “city”, “second degree”, “whole country”, “government office”, “hospital”, “institution”, “kindergarten”, “domicile”, “Chinese”, “art”, “engineering”, “nurse”, “pressure”, “agreement”, “experience”, “kindergarten teacher”, “counselor”, “downtown”, “Tianjin”, “other province”, “music”, “English”, “news”, “county town”. 94 Healthcare 2021,9, 846 5.2. Construction of Evaluation Model for Supporting Policy Measures An evaluation model for supporting policy measures is constructed here to evaluate and analyze the public support degree of the four types of measures summarized by the above PMC index model to study the social effects of the implementation of the employment promotion policy for college graduates. 5.2.1. Constructing Evaluation Dimension The degree of support for policy measures needs to be analyzed from multiple dimensions, including the theoretical goals of the policy measures, the people’s expectations of the policy measures, and the specific implementation methods of the policy measures. Most of the previous studies analyzed the policy support degree from one dimension (the theoretical objectives of the policy [ 32 ], the expectations of the masses [ 33 , 34 ], the policy means [ 35 ], etc.). The coverage of the policy is relatively narrow and lacks objectivity, which affects the scientific statistical results. In order to improve the credibility of the research results, this paper refers to the various evaluation dimensions adopted by the existing research and redefines the evaluation dimensions. Starting from multiple dimensions, it analyzes the degree of public support for various measures of college graduate employment promotion policies. The dimensions are shown in Table 7. Table 7. Evaluation dimension. Evaluation Dimension Comments Dimension Definition Theoretical objectives The theoretical effect to be achieved at the government level under the preset expectations of policy measures Since the epidemic is so severe this year, it is necessary to introduce policies to ensure employment. Expectations of the masses Expected effects of policy measures at the public level With postgraduate enrollment expansions, the graduate degree will be worthless in the future. Implementation means Specific implementation methods and processes of policy measures The policy was issued too late, the school has already sent the files back. Target groups The main body of policy measures Hope this policy is not just for fresh graduates. 5.2.2. Constructing Comment Topic Identification System As netizens often evaluate policy measures from different positions and perspectives, each comment may correspond to different evaluation dimensions. This paper uses the framework semantic dictionary matching method, takes the policy review subject word dictionary as the label system, and completes the identification of the corresponding dimensions by extracting and matching the evaluation words of comments. Among them, the policy review topic identification word dictionary is mainly generated based on the frequency of keyword in the comments combined with manual selection. Due to the large number of identified words, the semantic logic induction method is used to summarize and refine it. Sixteen themes are generated: “national condition”, “human resource”, “work treatment”, “work intensity”, “learning form”, “school roll”, “employment agreement”, “employer”, “position”, “examination”, “enrollment”, “region”, “education”, “subject”, “student type”, and “applicable condition”. Combining the evaluation dimension system constructed in Table 7 and the corresponding 16 themes with 4 evaluation dimensions, a comment topic identification system is obtained as shown in Table 8. This can avoid semantic confusion caused by a large number of topic words, thereby improving the data structure and clarifying the evaluation dimension to which the text belongs. 95 Healthcare 2021,9, 846 Table 8. Comment topic identification system. Dimension Theme Identification Word Theoretical objectives National condition employment rate, unemployment rate, epidemic, housing price Human resource labor force, talent, young people, quality Expectations of the masses Work treatment salary, treatment, subsidy, welfare Work intensity pressure, workload, energy Implementation means Learning form full-time, part-time School roll file, student source, registered residence, origin, household registration, domicile, Employment agreement agreement, tripartite agreement, contract Employer kindergarten, enterprise, university, government office, hospital, institution, vocational school, elementary school, college, unit Position nurse, kindergarten teacher, teacher, civil servant, counselor, head teacher, research assistant, special post teacher Examination written examination, interview, preliminary examination, reexamination Enrollment quota, proportion Target groups Region Shandong, Wuhan, Beijing, rural area, Chongqing, Sichuan, Guangdong, Anhui, Tianjin, area, Shanghai, Henan, whole country, other province, city, downtown, region, county town, township Education bachelor, junior college, Doctor, Master, second degree, college promotion Subject normal major, mathematics, music, Chinese, English, news, art, engineering, preschool education Student type fresh graduate, poor student, doctoral student, postgraduate, undergraduate, junior college student, previous graduate Applicable condition age, qualification, threshold, condition, experience, ability, accomplishment, fractional line, level The above-mentioned comment topic identification system is used to map comments to different evaluation dimensions. By identifying and matching comments, a total of 51,567 pieces of comments related to 4 evaluation dimensions were extracted. The specific results are shown in Table 9. 96 Healthcare 2021,9, 846 in the future, we will collect data from all countries, conduct targeted research, and give corresponding suggestions. Author Contributions: T.C. described the proposed framework and wrote the whole manuscript; J.R. implemented the simulation experiments; L.P. and J.F. collected data; J.Y. and G.C. revised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research is supported by the National Social Science Foundation of China (Grant No. 20BTQ059), the Project of China (Hangzhou) Cross-border E-commerce College (No.2021KXYJ07), the Key Project of Zhejiang Province Education Science Planning in 2021 (Grant No. 2021SB103), the General Scientific Research Project of Professional Degree Postgraduates of Zhejiang Education Department in 2020 (Grant No. Y202045139), the Scientific Research Project of Zhejiang Education Department (Grant No. Y201737899), the Contemporary Business and Trade Research Center and Center for Collaborative Innovation Studies of Modern Business of Zhejiang Gongshang University of China (Grant No. 14SMXY05YB), as well as the Characteristic & Preponderant Discipline of Key Construction Universities in Zhejiang Province (Zhejiang Gongshang University-Statistics). Institutional Review Board Statement: Not applicable. 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[CrossRef] [PubMed] 104 healthcare Article Assessing the Knowledge, Attitudes and Practices of COVID-19 among Quarantine Hotel Workers in China Yi-Man Teng 1,†, Kun-Shan Wu 2,*,†, Wen-Cheng Wang 3and Dan Xu 1 Citation: Teng, Y.-M.; Wu, K.-S.; Wang, W.-C.; Xu, D. Assessing the Knowledge, Attitudes and Practices of COVID-19 among Quarantine Hotel Workers in China. Healthcare 2021,9, 772. https://doi.org/ 10.3390/healthcare9060772 Academic Editors: Thomas Garavan, Eduardo Toméand Ana Dias Received: 26 May 2021 Accepted: 17 June 2021 Published: 21 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 College of Modern Management, Yango University, Fuzhou 350015, China; [email protected] (Y.-M.T.); [email protected] (D.X.) 2Department of Business Administration, Tamkang University, Taipei 251301, Taiwan 3College of Innovation and Entrepreneurship Education, Yango University, Fuzhou 350015, China; [email protected] *Correspondence: [email protected] † Equal first authorship. Abstract: During the pandemic, quarantine hotel workers face a higher risk of infection while they host quarantine guests from overseas. This study’s aim is to gain an understanding of the knowledge, attitudes, and practices (KAP) of quarantine hotel workers in China. A total of 170 participants took part in a cross-sectional survey to assess the KAP of quarantine hotel workers in China, during the COVID-19 pandemic. The chi-square test, independent t-test, one-way analysis of variance (ANOVA), descriptive analysis, and binary logistic regression were used to examine the sociodemographic factors associated with KAP levels during the COVID-19 pandemic. The results show that 62.41% have good knowledge, 94.7% have a positive attitude towards COVID-19, but only 78.2% have good practices. Most quarantine hotel workers (95.3%) are confident that COVID-19 will be successfully controlled and that China is handling the COVID-19 crisis well (98.8%). Most quarantine hotel workers are also taking personal precautions, such as avoiding crowds (80.6%) and wearing facemasks (97.6%). The results evidence that quarantine hotel workers in China have acquired the necessary knowledge, positive attitudes and proactive practices in response to the COVID-19 pandemic. The results of this study can provide a reference for quarantine hotel workers and their targeted education and intervention. Keywords: COVID-19; quarantine hotel workers; knowledge; attitudes; practices 1. Background The COVID-19 pandemic has made a significant impact on the health and safety of each country’s population, as well as ongoing effects to their economies and societies. The World Health Organization has declared the pandemic a public health emergency of global concern [ 1 ]. As positive cases of COVID-19 escalate, hospitals face problems with overcrowding and insufficient isolation space [ 2 ]. Australia enforces home-isolation for confirmed cases with mild symptoms and suspected cases; however, this ultimately increases the risk of infection to other household members [ 3 ]. To prevent this interaction, they propose that COVID-19 patients with mild symptoms isolate themselves by staying in a hotel. In addition, due to increasing concerns regarding transmitting the virus to their families, healthcare workers [4] need temporary quarantine accommodation. To mitigate the pandemic, countries around the world have implemented safety measures such as lockdown, social distancing, and mandatory 14-day quarantine periods for citizens and foreign visitors arriving from abroad [ 5 ]. The latter resulted in a demand for designated quarantine hotels, as this is where the majority of incoming residents and visitors will stay. As a result, many governments have expropriated hotels to be used as temporary quarantine accommodation: the ‘quarantine hotel.’ Quarantine hotels are a community-based public health intervention designed to mitigate the spread of COVID-19 Healthcare 2021,9, 772. https://doi.org/10.3390/healthcare9060772 https://www.mdpi.com/journal/healthcare 105 Healthcare 2021,9, 772 within the community [ 6 ]. The use of quarantine hotels to isolate tourists and returning residents for medical observation over a 14-day period is the hotel industry’s contribution to the control of COVID-19 [ 7 ]. Some scholars also argue that quarantine hotels reframe the taken-for-granted business model and goes beyond basic cleaning and hygiene standards to devote greater attention to the protection and safety of the quarantine guests’ physical and psychosocial needs, and better fulfill stakeholder demands [8]. Currently, in China, the COVID-19 epidemic is well-controlled; however, confirmed cases from overseas continue to increase [ 9 ]. During the pandemic, while hosting quarantine guests from overseas, quarantine hotel workers face a much higher risk of infection [ 6 ] as COVID-19  s main route of transmission is through respiratory droplets and direct contact with confirmed cases. Additionally, the quarantine hotel workload includes following an operation guide, complying with high-standard anti-epidemic and disinfection measures, and implementing quarantine services. The challenge for quarantine hotel workers is not only the increasing workload created by the quarantine hotel operation, but also high psychological stress associated with job insecurity, risk of exposure, and contagion for themselves, their friends, and families. Public health education was evidenced to be the significant measure to mitigate the spread of the epidemic during the SARS, MERS, and COVID-19 pandemics [ 10 , 11 ]. Previous literature proposes webinars (web-based seminars) as a public health educational tool, and provide a viable method of instruction and education for school personnel who are interested in strategies for improving a school’s wellness environment [ 12 ]. Recently, some scholars have also evidenced that webinars offer clear and actionable information to school staff about disease characteristics, adoptable preventive measures, and early detection and control of COVID-19 in primary schools [ 13 ]. Public health education may improve the effectiveness of preventive measures in terms of transmission of COVID-19 and other viruses. To effectively curb the COVID-19 crisis, countries worldwide are continuously promoting different unprecedented preventive measures, including appropriate personal hygiene and public health measures [ 14 ]. Incorrect knowledge toward the diseases affects people’s incorrect attitude and practices directly raise the risk of infection. Knowledge, attitudes, and practices (KAP) is a significant educational tool for public health and plays an integral role in determining a society’s readiness to accept behavioral change measures from health authorities [ 15 ]. Referring to the articles, people’s KAP towards COVID-19 largely affected adherence to control measures in accordance with KAP theory [ 16 – 18 ]. According to the previous studies, assessing the KAP toward COVID-19 would assist in providing better insight to address poor knowledge of COVID-19. This also offers the development of preventive strategies and health promotion programs [ 11 , 19 ]. Previous studies have evidenced the relation of a higher level of knowledge and the practice of preventive measures, as well as the positive relation of attitudes and preventive behaviors [ 20 – 22 ]. In the latest articles of KAP regarding COVID-19, they all demonstrated collecting KAP information has long been useful for informing prevention, control, and mitigation measures during the epidemic outbreaks [23]. Prior research provides evidence that the level of KAP possessed by inhabitants dictates the success of the adopted measures [ 15 , 18 , 24 – 26 ]. Recently, there are some studies investigated KAP towards COVID-19 in different group, such as general residents [19,26–36], healthcare workers [ 24 , 37 – 41 ], students [ 9 , 42 – 48 ], patients [ 49 ], hospital visitors [ 50 ] and slums [ 51 , 52 ], during the COVID-19 pandemic. Most of KAP studies regarding COVID-19 discuss the group of general residents. The results from these studies found that residents with a high level of knowledge about COVID-19 and positive attitudes toward it tended to have better preventive behaviors and behavioral compliance [ 19 , 26 ]. Furthermore, there are fewer articles that discuss KAP toward the COVID-19 system review and future direction [53,54]. Now, the increased trend in confirmed cases in China indicates they are coming from overseas. There is an urgent need to grasp quarantine hotel staff awareness of COVID-19 106 Healthcare 2021,9, 772 at this critical time, as they are providing service to host overseas quarantine guests. To the best of our knowledge, there is no published research concentrated on the KAP of quarantine hotel workers. The literature lacks an examination of quarantine hotel workers’ KAP toward COVID-19 from this perspective and has rarely discussed targeted education and intervention for quarantine hotel workers in order to comply with pandemic control measures. If the quarantine hotel workers’ KAPs are concerned about the virus and factors that affect their attitude and behavior, then this information can inform relevant training and policies during their work and guide them in prioritizing protection and avoiding occupational exposure. To date, peer-reviewed COVID-19 KAP surveys have comprised of a brief online survey among ordinary residents, healthcare workers, adults, students, patients, hospital visitors, and slums, and these surveys are not relevant in hospitality industry settings. To facilitate the management of the COVID-19 pandemic in the hospitality industry, there is an imperative need to grasp the quarantine hotel workers’ awareness of COVID-19 at this critical time. This study aims to assess quarantine hotel workers’ KAP towards COVID-19 through an online questionnaire survey in China. The implication of this study is to anticipate to guide quarantine hoteliers to develop the key skills in the hotel industries for medical education, as well as anti-epidemic and disinfection standards for their staff during and post-pandemic. 2. Materials and Methods 2.1. Study Design This cross-sectional study applied convenience sampling to collect samples from the quarantine hotel employees in Xiamen, Fujian Province, China, during the COVID-19 pandemics, from 20 May to 10 June 2020. There are approximately 50 quarantine hotels in Xiamen, as it is the only city in the Fujian Province with airports receiving international flights. The participating staff came from seven hotels. We called the HR manager of the quarantine hotel and asked them whether they would join the survey. Finally, the HR managers of seven hotels agreed to post the one-page recruitment poster on their WeChat (similar to WhatsApp) employee group chat and invited employees to participate in the survey. The advertisement included a brief introduction, background information, purpose, procedures, declarations of anonymity and confidentiality, and the voluntary nature of taking part. The quarantine hotel workers who understood the content of the survey and agreed to participate in the study were instructed to complete the questionnaire via clicking on the link or scanning the QR code. 2.2. Study Instrument The questionnaire consisted of four sections: (1) demographics—this surveyed participants’ sociodemographic information, including gender, age, education, and monthly income; (2) knowledge about COVID-19; (3) attitude toward COVID-19; and (4) practices relevant to COVID-19. To measure COVID-19 knowledge, 12 items were adapted from Zhong et al. [ 26 ]. There were four items regarding clinical presentations (K1–K4), three regarding transmission routes (K5–K7), and five regarding prevention and control (K8–K12). ‘True,’ ‘false,’ or ‘I don’t know’ responses were offered for these items. Correct answers scored ‘1  and incorrect/unknown answers scored ‘0.’ The score total range for knowledge items was 0–12, with higher scores indicating better knowledge about COVID-19. Bloom’s cut-off of 80% (≥9.6) was used to determine a better knowledge [55]. Attitudes were measured with a two-item scale developed by Zhong et al. [ 26 ]. Participants were asked to state their level of agreement on the successful control of COVID-19 (1 = agree; 0 = No/I don’t know), and confidence in winning the battle against the virus (1 = yes; 0 = No). An ‘I don’t know’ response was considered as a lack of agreement and thus, ‘No’ and ‘I don’t know’ were coupled, as per previous studies [ 14 , 40 ]. In addition, the combinations of responses were considered for each participant. The attitude of the partici107 Healthcare 2021,9, 772 pants who agreed that COVID-19 could be successfully controlled and were confident that China could overcome the pandemic scored ‘1  and were labeled as ‘optimistic attitude’ [ 26 ]. ‘No’ or ‘I don’t know’ responses scored ‘0and were labeled ‘negative attitude’ [14]. Practice toward COVID-19 was measured with a two-item scale that was developed by Zhong et al. [ 26 ]. Participants were asked to state their current behaviors, e.g., going to a crowded place and/or wearing a mask when going out (yes = 1; No = 0). Participants who agreed they had not been to any crowded places and wore a mask when leaving their home scored ‘1  and were labeled as ‘good practice’ toward COVID-19. The responses that disagreed scored ‘0and were labeled as ‘poor practice’ [14]. 2.3. Statistical Analysis The data were organized and analyzed using IBM SPSS Statistics (Statistical Package for the Social Sciences) 22.0 software (IBM, Armonk, NY, USA). The chi-squared test, independent t-test, and ANOVA with multiple comparisons between each two categories were done by post hoc analysis. Least significant difference (LSD) was applied to find the differences in KAP between groups for selected demographic variables. To identify related factors, the response binary logistic regression analysis was applied and expressed as odds ratio (OR) and 95% confidence interval (CI), with a significance level of 0.05 (twotailed). For the final model, the Hosmer–Lemeshow test [ 56 ], which measures goodness of fit (p-value > 0.05), was considered an appropriate logistic regression model. p< 0.05 was considered to indicate significance in all tests. Internal consistency of the questionnaire’s knowledge section revealed Cronbach’s alpha as 0.69, which confirms acceptable internal consistency. 2.4. Ethical Consideration According to the relevant laws and regulations of China and the guidelines of Yango University, an ethics approval was not required for this non-interventional study (e.g., surveys). Nevertheless, after quarantine hotel managers agreed to participate in this study, and ethical approval clearance and informed consent clearance were approved by the Luo, Zhong You, Executive principle of Yango University; hence, an ethical approval was expected. After expressing the principles of Helsinki Declaration, the participants were informed of the purpose of the research and expressed their informed consent. Participants were made clear that their participation is voluntary. The study was harmless to the participants, as no names were used and all data were analyzed anonymously in order to maintain anonymity. 3. Results 3.1. Respondent Characteristics In terms of demographics among 170 participants, there were slightly more female respondents in this study (n= 99, 58.2%) than there were male (41.8%). In terms of ages, 90 participants were Millennials (53.0%) and 32 participants were Generation Z (18.8%). In total, 103 participants (50.6%) had a junior college and above degree. In terms of departments, 28.8% participants were frontline employees (including front desk and housekeeping departments) and 71.2% participants were logistics support employees (including food & beverage, administration, and security departments). One-hundred twenty-eight participants (75.3%) indicated that their individual monthly income was 6000 RMB or below. 3.2. Assessment of COVID-19 Knowledge Results of the knowledge assessment of quarantine hotel workers regarding clinical presentations, transmission routes, and prevention and control of COVID-19 are shown in Table 1. The mean COVID-19 knowledge score for quarantine hotel workers was 9.78 (Standard deviation: 1.61, range: 0–12). The rate of overall correct answer to COVID-19 knowledge was 81.5% (9.78/12 × 100). The rate of overall correct answer for all quarantine hotel workers ranged between 23.5% and 98.2%. Most workers of the quarantine hotel 108 Healthcare 2021,9, 772 (98.2%) recognize that human beings who had contact with confirmed cases should be isolated immediately for 14 days. Even so, people are obviously confused about the spread of virus. When asked if eating and contacting wild animals would cause infection, only 23.5% answered correctly (Table 1). Table 1. Responses to the questionnaire on COVID-19 KAP. Items Correct Answer Rate (n;%) Incorrect Answer and ‘I Don’t Know’ Rate (n;%) K1. The main clinical symptoms of COVID-19 are fever, fatigue, dry cough, and myalgia. 159 (93.5) 11 (6.5) K2. Unlike the common cold, nasal congestion, runny nose, and sneezing are less common among people infected with COVID-19 virus. 113 (66.5) 57 (33.5) K3. At present, there is no effective treatment in COVID-19, but early symptomatic treatment can help most patients recover from infection. 152 (89.4) 18 (10.6) K4. Not all persons with COVID-19 will develop into severe cases. Only those who are elderly, have chronic illnesses, and are obese are more likely to be severe cases. 113 (66.5) 57 (33.5) K5. Eating or contacting wild animals would result in the infection by the COVID-19 virus. 40 (23.5) 130 (76.5) K6. Persons with COVID-19 cannot pass the virus to others when a fever is not present. 130 (76.5) 23.5 (4.0) K7. The COVID-19 virus spreads via respiratory droplets from infected individuals. 162 (95.3) 8 (4.7) K8. Ordinary residents can wear general medical masks to prevent infection from the COVID-19 virus. 162 (95.3) 8 (4.7) K9. It is not necessary for children and young adults to take measures to prevent infection from the COVID-19 virus. 156 (91.8) 14 (8.2) K10. To prevent infection by COVID-19, individuals should avoid going to crowded places such as train stations and avoid taking public transportation. 150 (88.2) 20 (11.8) K11. Isolation and treatment of people who are infected with the COVID-19 virus are effective ways to reduce the spread of the virus. 159 (93.5) 11 (6.5) K12. People who have contact with someone infected with the COVID-19 virus should be immediately isolated in a proper place. In general, the observation period is 14 days. 167 (98.2) 3 (1.8) Attitudes Answer yes rate (n; %) Answer no rate (n;%) A1. Do you agree that COVID-19 will finally be successfully controlled? 162 (95.3) 8 (4.7) A2. Do you have confidence that China can win the battle against the COVID-19 virus? 168 (98.8) 2 (1.2) Practice Answer yes rate (n; %) Answer no rate (n;%) P1. Have you been to any crowded places in recent days? 33 (19.4) 137 (80.6) P2. Do you wear a mask when you go out in recent days? 166 (97.6) 4 (2.4) The independent t-test and one-way ANOVA analysis were used to assess the differences in knowledge scores among different demographic characteristics. The results showed that there were no significant differences in knowledge scores for all demographic variables (gender, age, education, department, and monthly income) (p> 0.05, Table 2). 109 Healthcare 2021,9, 772 Table 2. Relationship between socio-demographic characteristics of the participants and their knowledge scores about COVID-19 (n= 170). Characteristics Category Number of Participants (%) Knowledge Score (Mean ±SD) t/F p-Value Gender Male a 71 (41.8) 9.54 ±1.52 −1.707 0.090 Female b 99 (51.2) 9.96 ±1.65 Age Generation Z a 32 (18.8) 9.22 ±2.15 2.529 0.083 Millennials b 90 (52.9) 9.88 ±1.51 Generation X c 48 (28.2) 9.98 ±1.28 Education MSB a 20 (11.8) 9.75 ±1.07 0.431 0.731 SHSVS b 47 (27.6) 9.85 ±1.43 JC c 57 (33.5) 9.91 ±1.84 UA d 46 (27.1) 9.57 ±1.70 Department Frontline a 49 (28.8) 9.86 ±1.37 0.385 0.701 Logistics support b 121 (71.2) 9.75 ±1.70 Income per month RMB #6000 and below a 128 (81.5) 9.85 ±1.53 −0.254 0.800 6001 and above b 29 (18.5) 9.93 ±1.46 Note: (1) Generation Z = Born 1996+; Millennials = Born 1977–1995; Generation X = Born 1965–1976. (2) MSB = Middle school and below; SHSVS = Senior high school/vocational school; JC = Junior college; UA = Undergraduate and above. (3) # Exclude ‘I don’t want to talk about it’ participants. (4) t= Student’s ttest, F = analysis of variance (ANOVA) test. (5) Multiple comparisons between each two categories are done by post hoc analysis (Least Significant Difference, LSD). Initially, most (8/12) knowledge questions about COVID-19 had a high accuracy rate (80% or more) (Table 1). As a result, a cut off knowledge score of ≤ 9 was set for poor knowledge and ≥ 10 for good (adequate) knowledge (Table 3). The study found that 62.41% of quarantine hotel workers have good (adequate) knowledge, which implies that a significant proportion of quarantine hotel workers have poor knowledge about COVID-19. Further, binary logistic regression analysis found that female quarantine hotel workers had higher odds of having good (adequate) knowledge at 10% significance level (Table 4). Table 3. Difference in quarantine hotel workers’ KAP toward COVID-19 by demographics (N= 170). Characteristics Knowledge Attitude Practice Poor (n; %) Good (n;%) χ2or t (p-Value) Negative (n; %) Optimistic (n;%) χ2or t (p-Value) Poor (n;%) Good (n; %) χ2or t (p-Value) Overall 64 (37.6) 106 (62.41) 9 (5.3) 161 (94.7) 37 (21.8) 133 (78.2) Gender 5.446 (0.020) 0.278 (0.598) 0.043 (0.837) Male 34 (47.9) 37 (52.1) 3 (4.2) 68 (95.8) 16 (22.5) 55 (77.5) Female 30 (30.3) 69 (69.7) 6 (6.1) 93 (93.9) 21 (21.2) 78 (78.5) Age 1.555 (0.460) 0.400 (0.819) 2.112 (0.348) Gen Z 15 (46.9) 17 (53.1) 1 (3.1) 31 (96.9) 10 (31.2) 22 (68.8) Millennials 31 (34.4) 59 (65.6) 5 (5.6) 85 (94.4) 18 (20.0) 72 (80.0) Gen X 18 (37.5) 30 (62.5) 3 (6.3) 45 (93.8) 9 (18.8) 39 (81.3) Education level 1.775 (0.620) 7.045 (0.070) 2.892 (0.409) Middle school and below 7 (35.0) 13 (65.0) 3 (15.0) 17 (85.0) 4 (20.0) 16 (80.0) Senior high school/ vocational school 19 (40.4) 28 (59.6) 4 (8.5) 43 (91.5) 8 (17.0) 39 (83.0) Junior college 18 (31.6) 39 (68.4) 1 (1.8) 56 (98.2) 11 (19.3) 46 (80.7) Undergraduate and above 20 (43.5) 26 (56.5) 1 (2.2) 45 (97.8) 14 (30.4) 32 (69.6) 110 Healthcare 2021,9, 772 Table 3. Cont. Characteristics Knowledge Attitude Practice Poor (n; %) Good (n;%) χ2or t (p-Value) Negative (n; %) Optimistic (n;%) χ2or t (p-Value) Poor (n;%) Good (n; %) χ2or t (p-Value) Department 0.024 (0.876) 0.094 (0.759) 1.873 (0.171) Frontline 18 (36.7) 31 (63.3) 3 (6.1) 46 (93.9) 14 (28.6) 35 (71.4) Logistics support 46 (38.0) 75 (62.0) 6 (5.0) 115 (95.0) 23 (19.0) 98 (81.0) Income per month RMB #1.300 (0.254) 0.496 (0.481) 1.138 (0.286) 6000 and below 43 (33.6) 85 (66.4) 5 (3.9) 123 (96.1) 24 (18.8) 104 (81.2) 6001 and above 13 (44.8) 16 (55.2) 2 (6.9) 27 (93.1) 8 (27.6) 21 (72.4) Knowledge score 8.25 ±1.53 10.71 ±0.68 −14.375 (0.000) 9.33 ±1.80 9.81 ±1.60 −0.860 (0.391) 9.78 ±1.57 9.78 ±1.63 0.006 (0.995) Note: (1) Knowledge section total scores range from 0–12, with a cut off level of ≤ 9 set for poor knowledge and ≥ 10 for good knowledge. (2) The attitude of the participants who agreed that COVID-19 could be successfully controlled and were confident about China winning against the pandemic scored ‘1’ and was labeled as ‘optimistic attitude’ toward COVID-19. Any other combinations of responses scored ‘0’ and were labeled as ‘negative attitude’ toward COVID-19. (3) The practice of the participants who agreed they had not gone to any crowded places and wore a mask when leaving home in recent days scored ‘1’ and was labeled as ‘good practice’ regarding COVID-19. Any other combinations of responses scored ‘0’ and were labeled as ‘poor practice’ regarding COVID-19. (4) # Exclude ‘I don’t want to talk about it’ participants. Table 4. Logistic regression analysis for factors associated with good knowledge and optimistic attitude regarding COVID-19 (N= 170). Characteristics Knowledge Attitude OR (95% CI) p-Value OR (95% CI) p-Value Gender (Reference: Male) Female 1.881 (0.922, 3.837) 0.082 0.522 (0.087, 3.139) 0.269 Age (Reference: Generation Z) Millennials 1.679 (0.605, 4.657) 0.319 9.066 (0.349, 235.311) 0.185 Generation X 1.517 (0.461, 4.989) 0.493 9.656 (0.288, 323.420) 0.206 Education (Reference: Middle school and below) Senior high school/ vocational school 1.292 (0.355, 4.702) 0.697 0.020 (0.001, 0.682) 0.030 * Junior college 1.272 (0.469, 3.452) 0.636 0.151 (0.009, 2.462) 0.184 Undergraduate and above 1.964 (0.797, 4.839) 0.142 1.001 (0.058, 17.420) 0.999 Department (Reference: logistics support department) Frontline 0.723 (0.333, 1.572) 0.413 0.516 (0.079, 3.372) 0.490 Income per month RMB (Reference: 6000 and below) # 6001 and above 0.460 (0.177, 1.197) 0.112 0.062 (0.003, 1.137) 0.061 Hosmer–Lemeshow goodness of fit statistic 8.277 0.309 2.050 0.979 Note: (1) #Exclude ‘I don’t want to talk about it’ participants; (2) * Statistically significant at p< 0.05. 3.3. Assessment of COVID-19 Attitudes To assess the attitudes toward COVID-19, two questions were used. One asked whether the COVID-19 epidemic would be successfully controlled, which the majority of the quarantine hotel workers agreed with (95.3%). Another asked whether they trusted China to be able to win its battle against the virus, which again, the majority of the quarantine hotel workers agreed with (98.8%). Overall, 94.7% of quarantine hotel workers had an optimistic (positive) attitude toward COVID-19, while 5.3% had a negative attitude (Table 3). In addition, attitudes toward COVID-19 were significantly associated with education level (Table 3). Quarantine hotel workers who had a senior high school/vocational school (vs. middle school and below, OR: 0.020, 95% CI = 0.001–0.682, p= 0.030) were more unlikely to have optimistic attitude toward COVID-19 (Table 4). 111 Healthcare 2021,9, 772 3.4. Assessment of COVID-19 Practices Quarantine hotel workers were asked two questions in assessment of practices relevant to COVID-19. The first question asked whether or not they agreed that they were avoiding crowded places in recent days; the second was whether or not they agreed that they were wearing face masks when outside the home in recent days. For the first question, 80.6% of quarantine hotel workers reported that they had been avoiding crowded places, whereas the remaining 19.4% had not been. Furthermore, 97.6% of quarantine hotel workers reported wearing a face mask when going out in public in recent days, whereas 2.4% indicated they did not. In addition, 78.2% of quarantine hotel workers had ‘good practice’ relevant to COVID-19. The remaining participants (21.8%) recorded ‘poor practice’ (Table 3). The practice relevant to COVID-19 was not significantly associated with all demographic characteristics (Table 3). 4. Discussion The outbreak of COVID-19 has sent the hotel industry into an unprecedented recession. In this context, the different management policies undertaken by hotel managers are determining the industry’s survival. Quarantine hotel workers’ adherence to control measures is essential, and is largely affected by their KAP towards COVID-19, in accordance with KAP theory. The understanding of quarantine hotel workers’ KAP toward the pandemic is helpful for hoteliers when addressing and implementing effective decision-making frameworks to ensure rapid response to unexpected events that challenge the solvency of their business. Assessing KAP related to COVID-19 provides greater insight, helping to address poor knowledge about the virus and assist with the development of preventive strategies and health promotion programs. KAP studies provide baseline information to determine the type of intervention that may be required to change misconceptions about the virus [ 14 , 15 , 24 ]. To date, there has been limited published data on quarantine hotel workers’ KAP toward COVID-19. Therefore, it is tremendously important to investigate the KAP of quarantine hotel workers to help guide these efforts. In China, the overall COVID-19 ‘correct’ knowledge rate among quarantine hotel workers is 81.5%, with an average score of moderate (9.78 ± 1.61). This knowledge score is higher than that of US residents (80%) [ 34 ] and the Palestinian population (79%) [ 57 ], but lower than the Chinese general population (90%) [ 26 ] and Tanzanian residents (84.4%) [ 11 ]. This study found that 62.41% of quarantine hotel workers in China have good (adequate) knowledge of COVID-19, which means there is a considerable proportion of quarantine hotel workers who have poor knowledge. Recently, the research results provide evidence that the level of knowledge regarding COVID-19 was proportional to age and years of education [ 58 ]. However, this empirical result reveals that there were no significant differences in knowledge toward COVID-19 scores for the demographic variables (age, education, department, and monthly income). That result is not consistent with other studies conducted worldwide, which shows knowledge was significantly differed across age, education level, and income [ 26 , 27 ]. Our study demonstrated the female quarantine hotel workers were more likely to have good (adequate) knowledge compared to men, which is consistent with the contentions of Zhong et al. [ 26 ] and Banik et al. [ 28 ], and is similar to a cross-cultural KAP study by Ali et al. [ 59 ]. This result may be explained by gender difference in related activities, and can also be attributed to the fact that women will experience higher family pressure in their role of caring for families. As a considerable amount of quarantine hotel workers in China have poor knowledge of COVID-19, quarantine hoteliers should provide extra medical education to identify microbiological characteristics and perform diagnosis, disinfection, and self-protection technology. Recently, some articles have advocated that hoteliers should improve the service of hygiene and cleaning, disinfection, and hygiene activities as the main contents response to the hotel industries development of post-COVID 19 [ 60 – 62 ]. Following the scholars, appropriate training in operation guides, complying with anti-epidemic and disinfection standards, and implementing quarantine services is also essential. 112 Healthcare 2021,9, 755 In this paper, we attempt to measure the relative efficiency in preventing the spread of COVID-19 using the data envelopment analysis (DEA) technique. In practice, the DEA technique has been widely used in various applications, including health industries [ 5 , 6 ], energy sectors [ 7 – 9 ], cement industries [ 10 ], agricultural production [ 11 , 12 ], and manufacturing sectors [ 13 ], and it has proven to be an effective approach in identifying the best practice frontiers. In the field of medical services, DEA was also widely used to measure the efficiency of hospitals in association with patient visits, surgeries, and discharges. For example, Khushalani and Ozcan [ 14 ] employed a dynamic network DEA to examine the efficiency of production quality in hospitals and found that urban and teaching hospitals were less likely to improve quality production efficiency. Deily and McKay [ 15 ] used efficiency scores obtained from a DEA analysis as explanatory variables to determine hospital efficiency. In other fields, Oggioni et al. [ 10 ] employed DEA to analyze efficiency by using energy as an input and one desired output accompanied by undesired outputs (CO 2 emissions). Mousavi-Avval et al. [ 11 ] and Mohammadi et al. [ 12 ] applied the DEA technique to measure the efficiency of agricultural production to identify wasteful energy. Vazhayil and Balasubramanian [ 9 ] showed that the weight-restricted stochastic DEA method was appropriate to optimize power sector strategies. To compare the mitigation efficiency among countries on a fair basis, the time period for each stage was calculated from the date of the first confirmed case in each country. The whole period covers 105 days from the first confirmed case and was divided into six stages. In addition to the measurement of overall efficiency covering 105 days, the efficiency at each stage was also evaluated. Firstly, the purpose of this article was to compare the relative efficiency of each country in mitigating the spread of the COVID-19 epidemic. Secondly, the trends of efficiency rank across stages for each country were analyzed. Eventually, an indicator for epidemic stability was developed to judge the status of epidemic stability for each country. 2. Research Methods To compare the relative efficiency in preventing and reducing the spread of COVID-19, a total of 23 countries were selected, including 19 countries in the G20 and four other representative countries, as listed in Table 1. The reason for the selection of Iran and Spain was due to their high levels of confirmed cases and deaths. Pakistan and Nigeria were chosen due to their large populations, which reached 220.9 million and 206.1 million, respectively, at the end of 2020 [16]. Table 1. The starting and ending dates of each stage for each country. Country Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 China 2019/12/31–2020/1/30 2020/1/31–02/14 02/15–02/29 02/30–03/15 03/16–03/30 03/31–04/14 Japan 01/15–02/14 02/15–02/29 03/01–03/15 03/16–03/30 03/31–04/14 04/15–04/29 Korea 01/20–02/19 02/20–03/05 03/06–03/20 03/21–04/04 04/05–04/19 04/20–05/04 USA 01/23–02/22 02/23–03/08 03/09–03/23 03/24–04/07 04/08–04/22 04/23–05/07 Australia, France 01/25–02/24 02/25–03/10 03/11–03/25 03/26–04/09 04/10–04/24 04/25–05/09 Canada 01/27–02/26 02/27–03/12 03/13–03/27 03/28–04/11 04/12–04/26 04/27–05/11 Germany 01/28–02/27 02/28–03/13 03/14–03/28 03/29–04/12 04/13–04/27 04/28–05/12 India 01/30–02/29 03/01–03/15 03/16–03/30 03/31–04/14 04/15–04/29 04/30–05/14 Italy 01/31–03/01 03/02–03/16 03/17–03/31 04/01–04/15 04/16–04/30 05/01–05/15 Russia, Spain, UK 02/01–03/02 03/03–03/17 03/18–04/01 04/02–04/16 04/17–05/01 05/02–05/16 Iran 02/20–03/21 03/22–04/05 04/06–04/20 04/21–05/05 05/06–05/20 05/21–06/04 Brazil, Pakistan 02/27–03/28 03/29–04/12 04/13–04/27 04/28–05/12 05/13–05/27 05/28–06/11 Nigeria 02/28–03/29 03/30–04/13 04/14–04/28 04/29–05/13 05/14–05/28 05/29–06/12 Mexico 02/29–03/30 03/31–04/14 04/15–04/29 04/30–05/14 05/15–05/29 05/30–06/13 Indonesia 03/02–04/01 04/02/04/16 04/17–05/01 05/02–05/16 05/17–05/31 06/01–06/15 Saudi Arabia 03/03–04/02 04/03–04/17 04/18–05/02 05/03–05/17 05/18–06/01 06/02–06/16 Argentina 03/04–04/03 04/04–04/18 04/19–05/03 05/04–05/18 05/19–06/02 06/03–06/17 South Africa 03/06–04/05 04/06–04/20 04/21–05/05 05/06–05/20 05/21–06/04 06/05–06/19 Turkey 03/12–04/11 04/12–04/26 04/27–05/11 05/12–05/26 05/27–06/10 06/11–06/25 The WHO [ 1 ] divided the stages of transmission into (1) no cases reported or observed (Stage 0); (2) imported cases (Stage 1); (3) localized community transmission (Stage 2); and (4) large-scale community transmission (Stage 3). As the date of the first confirmed case 119 Healthcare 2021,9, 755 varied across countries, the period of each stage was not based on the same date among these countries but was calculated instead from the date of the first confirmed case in each country. The date of the first confirmed case was identified based on the daily situation report released by the WHO [ 1 ] starting on 21 January 2020. Among the 23 counties selected, China, Japan, and Korea reported having confirmed cases of COVID-19 before 21 January 2020. The information released from the WHO [ 1 ] demonstrated that some cases of pneumonia of unknown etiology were detected in Wuhan City, Hubei Province, China, on 31 December 2019. On 7 January 2020, a new type of coronavirus was isolated and identified. Thus, the first case in China may be considered to have occurred at the end of 2019. According to the WHO [ 1 ], the first confirmed cases of COVID-19 in Japan and Korea were reported on 15 and 20 January 2020, respectively. The overall efficiency was compared based on the whole period covering 105 days since the first confirmed case for each country. The development process of COVID-19 spread was separated into 6 stages. As the number of new confirmed cases reported in earlier days is much lower, Stage 1 covers the first 30 days after the first confirmed case in each country. Each stage from Stage 2 to Stage 6 covered 15 days. The starting and ending dates of each stage for each country are listed in Table 1. 2.1. The DEA Model In this paper, the DEA model was employed to measure the mitigation efficiency regarding the spread of COVID-19 at each stage for each country. The DEA model, proposed by Charnes et al. [ 17 ] based on the frontier production function defined by Farrell [ 18 ], is a nonparametric technique for measuring the relative efficiency of each decision-making unit (DMU) [ 19 ]. The mitigation of COVID-19 transmission in each country was executed by a technology whereby Ncountries in terms of DMUs transform a non-negative vector of multiple inputs, denoted x= ( x1 , ... , xm ) ∈m + , into a non-negative vector of multiple outputs, denote y= ( y1 , ... , ys ) ∈s + . This paper employed the basic DEA model of Charnes, Coopers, and Rhodes (CCR) to calculate the efficiency of COVID-19 transmission mitigation. The CCR model, under the hypothesis of constant returns to scale, is expressed as follows: Min θ s.t.θx0−Xλ≥0 Yλ≥y0 λ≥0 (1) where y0 is the output, x0 is the input, Xand Y are the datasets in the matrices, λ is a semipositive vector, and θrepresents the technical efficiency. After the efficiency at each stage was obtained, Pearson correlation tests were conducted between the different stages at a p-value < 0.01 to examine the variation in efficiency ranks across stages. The correlation tests were used to explain the impact of the efficiency ranks at previous stages on subsequent stages. In this paper, epidemic stability (ES) is defined as the recovery status from the epidemic, and the indicator ES is presented by measuring the average increase in the proportion of confirmed cases to population (PCCP) during the period of the last day of Stage 6 and a day designated to restart the economy, expressed as follows: ES =Sf−S0 Δt(2) where Sf and S0 denote the PCCP on the last day of Stage 6 and the designated day, respectively, and Δt represents the period between the two dates. 2.2. The Variables Efficiency, described as the relative performance regarding the reduction in COVID-19 transmission, was measured in this paper using the DEA method and is stated in the form 120 Healthcare 2021,9, 755 of an output/input ratio. The objective of the authority administration was to minimize the total confirmed cases that occurred in each stage with a given amount of resources used. Cooper et al. [ 19 ] suggested that the DEA technique can be easily applied to a multiple input–output framework to compare the relative efficiency among various DMUs. The information produced from the DEA is valuable for identifying specific efficient units for future learning [20]. Neiderud [ 21 ] suggested that the rise of megacities may yield potential risks for new epidemics and become a threat in the world. The high human population density and close human-to-human contact are major sources for the rapid spread of respiratory diseases or avian flu. The growth and density of the human population may work as an incubator for infectious diseases, and urbanization as a driver of disease may have a negative effect on public health [ 22 , 23 ]. Thus, variables including (1) newly confirmed cases n, (2) population density d, and (3) urbanization degree ufor each country were employed to measure the relative efficiency. As more confirmed cases represent less efficiency, newly confirmed cases nwas treated as an input variable in Equation (1) to measure mitigation efficiency. In essence, the higher the population density and urbanization of a country are, the greater the chance of infection is. Thus, population density dand urbanization degree uwere treated as output variables in Equation (1) for the measurement of mitigation efficiency. 2.3. Data Collection The data for accumulated confirmed cases were extracted from the daily situation reports from the WHO [ 1 ], and the total confirmed cases in each stage were calculated by the difference in the accumulated confirmed cases on the last day of each stage and the previous stage. The population density data for each country were provided by Worldometer [ 24 ], and the urbanization degree data were extracted from the World Bank [ 16 ]. The descriptive statistics for the total accumulated confirmed cases across the 6 stages (i.e., 105 days since the first confirmed case), population density and urbanization degree are presented in Table 2. By the end of Stage 6 (i.e., 105 days since the first confirmed case), the USA had 1,193,452 confirmed cases, ranking at the top of the 23 countries, while Australia had the lowest number (6914) of confirmed cases. Korea had the highest population density at 527.30 persons per km 2 , while Australia had a much lower population density at 3.32 persons per km 2 . Argentina had the largest urbanization degree at 92% and ranked at the top. In contrast, the urbanization degree of India was much lower than the average of 71.48% based on the other countries and was only 34%. Table 2. Descriptive statistics of study variables. Statistics Total Confirmed Cases n Population Density d(Person Per km2) Urbanization Degree u(%) Max. 1,193,452 527.30 92.00 Min. 6914 3.32 34.00 Average 190,093 151.40 71.48 Standard deviation 260,495 146.28 15.45 The efficiency score was calculated through the assistance of the software DEA solver 13. 3. Results The efficiency of COVID-19 mitigation covering the first 105 days after a confirmed case for each of the countries is depicted in Figure 1. Australia and Korea rank at the top in terms of mitigation efficiency. In contrast, the USA ranks at the bottom, followed by Brazil and Russia. The major cause affecting the efficiency rank may be attributed to the number of total confirmed cases occurring over the whole period. The total confirmed cases in Australia and Korea in the whole period (covering 105 days since the first confirmed case) 121 Healthcare 2021,9, 755 were only 6667 cases and 10,801 cases, respectively, while the USA, Brazil, and Russia had 1,193,452; 739,503, and 272,043 cases, respectively. Figure 1. Mitigation efficiency scores among the 23 countries. The efficiency scores and ranks at each stage for each country were also calculated according to Equation (1). Based on the shape of the efficiency ranking trend, these countries were classified into five types, as depicted in Figure 2.       6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 5DQNV &KLQD .RUHD ,WDO\ 6SDLQ 8. *HUPDQ\ )UDQFH         6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 5DQNV -DSDQ $XVWUDOLD       6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 5DQNV 5XVVLD ,QGLD       6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 5DQNV 86$ ,UDQ 7XUNH\ ,QGRQHVLD 3DNLVWDQ 6RXWK$IULFD            6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 6WDJH 5DQNV 1LJHULD 0H[LFR 6DXGL$UDELD 7\SH 7\SH 7\SH 7\SH 7\SH Figure 2. The trends in efficiency rank for countries of Types (1)–(5). 122 Healthcare 2021,9, 755 3.1. Type (1): An Inverted U-Shaped Pattern Including Korea, China, Italy, Spain, UK, Germany, and France This pattern in the efficiency rank trends was characterized by a continual decline in mitigation efficiency from Stage 1, which, after reaching the lowest point in the efficiency ranks, continued to improve until the last stage (Stage 6). Efforts to mitigate newly confirmed cases through the implementation of response strategies may have eventually achieved a certain effect. In essence, the mitigation efficiency in Type (1) gradually deteriorated in the middle stages. Passing through the peak of daily new confirmed cases, the COVID-19 transmission was then reduced, and the efficiency started to improve through the last stage. For example, Italy ranked 14th in Stage 1 and then dropped to 20th in Stage 2. Italy then reached a peak of daily confirmed cases, amounting to 6557 cases on 22 March 2020, which occurred in Stage 3. After Stage 3, the COVID-19 transmission in Italy improved, and the efficiency rank rose to 13th place in Stage 6. The efficiency ranks for China after Stage 3 and for Korea after Stage 2 showed great improvement and attained a relatively more stable state. China ranked 21st place and 22nd place at Stage 1 and Stage 2, respectively, but the efficiency rank was improved to 2nd place at Stage 4 and 3rd place at Stages 5 and 6 through a great number of emergency response strategies. Similar to China, Korea ranked in 10th place and 13th place for mitigation efficiency at Stages 1 and 2, respectively, and the efficiency improved to 6th place at Stage 3 and first place at Stage 4, which was subsequently maintained until the final stage. The other countries showed similar processes, but the degree of efficiency improvement was different. 3.2. Type (2): An Inverted N-Shaped Pattern Including Japan and Australia In this type, the efficiency rank fluctuated across stages, with initial improvements followed by deterioration in the middle stages, but eventually, the efficiency rank improved in the final stages. For example, the efficiency rank for Japan improved continuously from 12th place in Stage 1 to 6th place in Stage 2 to first place in Stage 3, then dropped to 4th place in Stage 4 and 6th place in Stage 5, and eventually improved to 4th place again. 3.3. Type (3): Continual Decreases in Efficiency Rank Including Russia and India The trend pattern in efficiency rank for Type (3) countries is characterized by the gradual deterioration in mitigation efficiency. The efficiency ranks are not bad in the earlier stages, but they worsen progressively. For example, Russia performed at the highest level regarding mitigation efficiency in Stage 1 and was ranked in first place. Unfortunately, Russia did not maintain this advantage, and its rank continued to deteriorate to 4th place in Stage 2 and, finally, to 21st place in Stage 6. 3.4. Type (4): U-Shaped Pattern Including the USA, Iran, Turkey, Indonesia, Pakistan, South Africa, Argentina, and Brazil This trend in the efficiency ranks is characterized by some improvements in mitigation efficiency in the middle stages that eventually rebound back to a worse state. For example, the response in the USA to avoid COVID-19 transmission was not bad in Stages 1 and 2, as it ranked in 8th place and 5th place, respectively. However, its efficiency continually and dramatically dropped after Stage 2 and fell to 23rd place (the bottom of the ranking) in Stages 5 and 6. The efficiency improvement from Stage 1 to Stage 2 in the USA may be attributed to its prompt travel restrictions on China from 2 February 2020 and additional travel restrictions on Iran, Italy, and Korea on 29 February [ 25 ]. The gradual deterioration in efficiency ranking in the later stages in the USA implies that its response strategies may be ineffective for avoiding the epidemic. The trend pattern in the efficiency ranking for Brazil provides a different story. From Stage 1 to Stage 6, the efficiency ranks for Brazil were not good. On 25 June 2020 (the final observation point in Stage 6) in Brazil, newly confirmed cases remained at a high level, amounting to 39,436 cases. This implies that the response strategies adopted by Brazil contained flaws. 123 Healthcare 2021,9, 755 3.5. Type (5): N-Shaped and W-Shaped Patterns Including Mexico, Nigeria, and Saudi Arabia An N-shaped pattern for Mexico and W-shaped patterns for Nigeria and Saudi Arabia were identified. At the middle stages, the efficiency ranks for these Type (5) countries fluctuated very much. For example, Mexico ranked 13th place at Stage 1 and then dropped and rose in the middle stages, eventually dropping again to 17th place at Stage 6. As the efficiency for these two patterns drops again in the last stages, this implies that the mitigation efficiency is not stable and that the future trends for these countries are not optimistic. To examine the impact of the efficiency rank at the previous stage on the subsequent stage, a Pearson correlation test of efficiency scores between different stages was conducted. The results are listed in Table 3. The correlation coefficient between Stage 1 and Stages 4–6 was very low, ranging from 0 to − 0.1433. In contrast, the correlation coefficient was 0.788 between Stage 4 and Stage 5, 0.760 between Stage 4 and Stage 6, and 0.983 between Stage 5 and Stage 6. Table 3 also shows that the greater the distance is between any two stages, the lower the correlation coefficient is. Table 3. Correlations of mitigation efficiency between different stages. Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 1 1 Stage 2 0.6739 *** 1 Stage 3 0.4048 ** 0.5666 *** 1 Stage 4 −0.1433 0.0210 0.4297 ** 1 Stage 5 −0.0982 0.1918 0.2002 0.7884 *** 1 Stage 6 −0.0824 0.1401 0.1783 0.7602 *** 0.9828 *** 1 **: p≤0.05; ***: p≤0.01. A numerical example is presented in this paper, in which it was proposed that the travel restrictions were lifted on the designed date of 27 June 2020; ES, Sf , S0 , and Δt were calculated according to Equation (2) for these 23 countries, and the results are listed in Table 4, where Sf and S0 are measured by cases per 100,000 persons, Δt in days, and ES by cases per 1,000,000 persons. The ranking of each country listed in Table 4 is based on the value of epidemic stability (ES). Table 4 indicates that India has the lowest value of S0 (PCCP in 105 days), amounting to 5.65 cases per 100,000 persons, a slightly lower value than that of China (5.81 cases per 100,000 persons). In contrast, Spain and Saudi Arabia have the highest values of S0 , amounting to 492.32 and 379.30 cases per 100,000 persons, respectively, which are much higher than the average of 172.19 cases per 100,000 persons. However, the ranking of the PCCP on 27 June 2020 ( Sf ) changes very much. China ranks at the top with the lowest Sf , amounting to 5.92 cases per 100,000 persons. The PCCP in India increases very much from 5.65 at S0 to 36.88 cases per million at Sf . The USA has the highest value at Sf , amounting to 727.37 cases per 100,000 persons. Table 4 also demonstrates that the ES in China, Japan, Korea, and Australia is much better than that in the other countries, amounting to 0.01, 0.46, 0.68, and 0.89 cases per million persons per day, respectively, during the period between the last day of Stage 6 and 27 June 2020. In contrast, the ES in Brazil, Saudi Arabia, South Africa, and the USA reaches 143.67, 93.97, 85.78, and 71.92 cases per million persons per day, respectively. Based on the values of ES, it is suggested that the future trends regarding the pandemic in Brazil, Saudi Arabia, South Africa, and the USA are not optimistic and are full of challenges. 124 Healthcare 2021,9, 755 Table 4. The epidemic stability for each country by rank. DMU S0SfΔtES Rank China 5.81 5.92 74 0.01 1 Japan 12.04 14.47 59 0.46 2 Korea 21.07 24.68 54 0.68 3 Australia 27.11 29.78 30 0.89 4 Nigeria 7.06 11.3 15 2.83 5 Indonesia 13.99 18.74 12 3.95 6 Germany 203.51 230.64 46 5.90 7 Italy 368.99 396.88 43 6.49 8 India 5.65 36.88 44 7.10 9 Spain 492.32 530.22 42 9.02 10 France 209.24 239.23 30 10.00 11 Turkey 227.25 230.63 2 16.92 12 Canada 180.16 271.9 47 19.52 13 Pakistan 54.12 90.04 16 22.45 14 UK 348.69 455.71 42 25.48 15 Iran 191.32 259.22 21 32.33 16 Mexico 103.91 157.41 14 38.21 17 Argentina 72.54 116.07 10 43.53 18 Russia 186.41 430.09 42 58.02 19 USA 360.56 727.36 51 71.92 20 South Africa 141.45 210.07 8 85.78 21 Saudi Arabia 379.3 501.46 13 93.97 22 Brazil 347.9 577.77 16 143.67 23 S0 : epidemic stability on the designated date (27 June 2020); Sf : the last day of Stage 6; Δt : the period between the designated date and the last day of Stage 6; ES: epidemic stability. 4. Discussion The DEA in this paper shows that Korea, Australia, and Japan had better mitigation efficiency by 27 June 2020, while the USA, Brazil, and Russia performed less efficiently and were ranked at the bottom. Ahn [ 26 ] suggested that the successful experience in Korea to counter COVID-19 spread may be attributed to the mass testing and effective contact tracking system. Individuals testing positive for the infection after viral tests were hospitalized at special facilities. The people who had been in contact with the infected were to remain self-quarantined for 14 days. The availability of personal protective equipment was ensured to have a sufficient supply to avoid further infection at the onset of COVID-19 in Korea. In contrast, the testing capacity has not been sufficient to support the policies of a gradual reopening of the economy planned in many US states [27]. 4.1. The Trend Patterns in Efficiency Ranks The trend patterns in efficiency ranks also revealed information about future trends regarding epidemic mitigation. Type (1) and Type (2) countries may have more optimistic chances regarding recovery from the spread of COVID-19, as the efficiency ranks of Type (1) and Type (2) countries were high in Stage 6. The Type (1) countries included the following seven countries: Korea, China, Italy, Spain, the UK, Germany, and France. In addition to Korea, the other countries implemented effective responsive strategies, including extensive viral tests, lockdowns, social distancing, temporary cessation of sports events, school closures, and wearing of masks. In China, testing policies were promoted by expanding the testing of individuals from persons with symptoms to the open public on 12 February 2020, and all levels of school were closed on 26 January 2020 [ 28 , 29 ]. China has successfully slowed the transmission of COVID-19 through a combination of lockdowns, viral tests, contacting tracing, and other minor strategies, including street sanitization, school closures, and wearing of masks. Strict lockdowns and strict checks to avoid close contact between people were implemented in China after the outbreak. In less than three months, China gradually eased the strict policy of the lockdown and started to motivate 125 Healthcare 2021,9, 755 the opening of economic activities. The strict lockdowns, wearing of masks, and social distancing implemented in China may be the major contributors to the effective prevention of transmission in a short time. In contrast, the response of European countries such as Italy was not as prompt and urgent as that in Korea or China, and their efficiency ranks after Stage 4 were worse. For example, schools in Italy closed on 2 March 2020, and people were asked to stay at home, with exceptions for daily exercise and grocery shopping, on 23 February 2020. However, the testing policy adopted in Italy focused on testing anyone with COVID-19 symptoms after 26 February [ 28 , 29 ]. However, the efficiency ranks for the UK in the later stages (Stages 4–6) were much worse than those of other European countries. In March 2020, the UK attempted to reduce the impact of COVID-19 by means of herd immunity, but later, it denied the claims of herd immunity and argued that herd immunity is a natural by-product of an epidemic [ 30 ]. Given this situation, the strategy to fight against the epidemic was delayed, and thus, the effect was reduced. Type (2) countries consisted of only Japan and Australia, with overall efficiency ranks of first and third place, respectively. In the middle stages, the efficiency ranks initially improved and then grew worse. A possible cause for these changes in efficiency ranks may be the low levels of viral testing in the earlier stages. Extensive viral tests were performed in Australia and amounted to nearly 1000 tests per 100,000 people in the population by 31 March 2020 [ 31 ]. This number continued to increase and reached 2081 tests per 100,000 people on 28 April 2020 and 3119 tests per 100,000 people on 9 May 2020 (the final observation point in Stage 6 for Australia). The high testing rate in Australia may have been a major factor in mitigating the increase in new cases and leading it to have the best overall efficiency among these 23 countries. In contrast, the trend in efficiency ranks for Type (3) countries showed a continual deterioration in mitigation efficiency. Compared to other countries, the coronavirus testing rate per capita in India was very low, reaching a total of 144,910 tests in a population with more than 1.3 billion people by 9 April 2020 [ 32 ]. On 14 May 2020 (the final observation point in Stage 6 for India), the viral testing rate was only 1.41 tests per 1000 people [ 29 ]. The low testing rate may be a key factor in explaining the good performance based on the high-efficiency ranking from Stage 1 to Stage 4. Without testing, no data are generated; thus, higher efficiency scores are obtained. As of 27 June 2020, the total number of confirmed cases in India reached 508,953, which was about 6.5 times the total number of confirmed cases of 78,003 during the entire period as of 14 May 2020. At the onset of the outbreak, Russia announced a temporary ban on Chinese citizens from entering Russia on 20 February 2020 [ 25 ]. This strategy may have been effective in preventing infection through imported cases from China in Stage 1 and Stage 2. Extensive testing had been conducted in Russia, including 0.32 tests per 1000 people on 5 March 2020, 1.12 tests per 1000 people on 22 March 2020, 4.38 tests per 1000 people on 4 April 2020, 11.06 tests per 1000 people on 16 April 2020, 27.04 tests per 1000 people on 2 May 2020, and 45.61 tests per 1000 people on 16 May 2020 (the last day of Stage 6). However, Russia’s health department admitted that the test kits were often wrong and provided false-negative results. Therefore, the tested people with the virus were allowed to go home and thus infected other people. Thus, the real number of infected individuals was more than triple the official figure [ 33 ]. The ineffective tests may explain the continual deterioration in efficiency scores for Russia. Type (4) countries contained the following nine countries: the USA, Iran, Turkey, Canada, Indonesia, Pakistan, South Africa, Argentina, and Brazil. If the current trends for these countries continue into the future, the outcomes do not look optimistic regarding the epidemic, and these countries need to devote more effort to improving mitigation in newly confirmed cases as their efficiency ranks were poor in the final stages. Some Type (4) countries lacked testing capacity in the earlier stages of the pandemic, and thus, the amount of testing that was performed was much lower than needed. Due to having less viral testing than the actual need, underestimation of newly confirmed cases may have 126 Healthcare 2021,9, 755 taken place and led to the illusion of efficiency improvement, but eventually, efficiency ranks dropped in the final stages. In the USA, the total number of tests performed relative to the size of the population before 7 March 2020 was very low, at less than 0.01 tests per 1000 people, and the situation gradually improved in March 2020 (in Stage 3). The testing rate increased to 0.23 tests per 1000 people by the end of March 2020 (in Stage 4) and then quickly increased to 10.43 tests per 1000 people on 16 April 2020 (in Stage 5). On the day of the final observation point in Stage 6 (7 May 2020), the testing rate rose to 24.63 tests per 1000 people, which seems to be a good figure compared to that of other countries. However, several experts have criticized the fact that the testing levels were not sufficient to meet the need for a gradual reopening by 1 May 2020 [ 27 ]. In addition, existing flaws in other response strategies also blocked improvements in the efficiency rank for the USA. For example, the US Centers for Disease Control and Prevention (CDC) emphasized the importance of mask-wearing, but Donald Trump continued to reject being photographed in public wearing a mask [ 34 ]. Some experts have suggested that the guidelines for mask-wearing have been confusing. Thus, many protesters across the country are described as people who refuse to wear a mask [35]. In fact, the USA has not been positively and seriously prepared for epidemic mitigation since the first confirmed case occurred on 23 January 2020. On 23 April 2020, Trump suggested injecting a powerful disinfectant into coronavirus patients as a possible cure for COVID-19. This news resulted in criticism from many scholars and reporters and disbelief and derision worldwide [36]. The trends in efficiency ranks for Type (5) countries, including Mexico, Nigeria, and Saudi Arabia, fluctuated more than those of the other country types. The testing rate in Mexico ranged from 0.01 to 3.1 tests per 1000 people during the whole period, which was much lower than that in other countries. Thus, the mitigation efficiency of Mexico ranked 17th among the 23 countries in Stage 5 and Stage 6. On 13 June 2020 (the final day of Stage 6 for Nigeria), the testing rate was 0.44 tests per 1000 people. Nigeria had a lower testing rate than Mexico, but the efficiency ranks for Nigeria were not bad. Thus, we reasonably suspect that the high-efficiency ranks of Nigeria may have been caused by an underestimation due to low viral testing rates. 4.2. The Correlation of Efficiency Ranks among Various Stages Table 3 indicates that the correlation coefficient between two adjacent stages was higher than that between two non-adjacent stages. The correlation coefficients between Stage 1 and each stage after Stage 3 were low and negative. The negative or near-zero correlation coefficients between Stage 1 and Stages 4–6 imply that the efficiency ranking of the sampled countries at Stages 4–6 had been reorganized and completely differed from that at Stage 1. This implies that at Stage 1, some countries started to implement effective response strategies such as extensive viral testing, lockdowns, wearing of masks, etc., to prevent the spread of COVID-19 and thus created improved effects at Stages 4–6. In contrast, some countries purposely neglected the serious and emergent impacts arising from COVID-19 spread and failed to take any measures in response to the emergence of the epidemic. On the other hand, the high correlation coefficients between Stage 4 and Stage 5, Stage 4 and Stage 6, and Stage 5 and Stage 6 imply that the relative efficiency ranks among these countries became stable because their response strategies had stabilized. The efficiency ranks in some countries showed a high degree of fluctuation across stages, especially the Type (5) countries. The high fluctuation in efficiency ranks implied that good efficiency rankings at a particular stage were only temporary and may have deteriorated in the next stage. The mitigation efficiency rankings for Type (3) countries continually worsened from Stage 1 to Stage 6. Thus, the Type (3) countries could not recover from the attack of COVID-19 in a short time and would have to adopt stricter response policies to mitigate the spread of COVID-19. Type (4) countries showed a U127 Healthcare 2021,9, 755 shaped pattern, demonstrating temporarily improved ranks in the middle stages, but eventually, the ranking regressed in the final stages. Both the inverted U-shaped (Type 1) and inverted N-shaped (Type 2) patterns in the trends in efficiency ranks seemed to be a good sign of improvement, as the efficiency ranks increased in the last stages. The probability of recovering from the attack of COVID-19 for Type (1) and (2) patterns is higher than that for other patterns. Nevertheless, the overall efficiency was calculated based on the whole period covering 105 days since the first confirmed case. The efficiency obtained was only temporary and could change for the better or worse if the assessment stage was extended to cover more days. 4.3. The Epidemic Stability At the beginning of June 2020, the infectious disease COVID-19 remained a high risk in the world, but many countries have since attempted to lift the state of lockdown, restart the economy, and take action, as their governments have considered that the number of confirmed cases was greatly reduced and that newly diagnosed cases may be considered sporadic cases. For example, Trump attempted to end the lockdown and the stay-at-home order and to reopen schools at the beginning of June 2020 [37]. There was a high correlation between the efficiency scores in two adjacent stages, but it was still difficult to predict the epidemic stability of the next stage based on that of the previous stage. Thus, the data of the newly confirmed cases for the current dates are only for reference to determine the timing of restarting the economy. This paper suggests that an epidemic stability indicator in combination with a trend pattern of efficiency ranks such as Type (1) or (2) may be employed to judge the appropriateness of any measures to ease the response strategies such as travel restrictions, stay-at-home orders, and mask-wearing. Low values of epidemic stability imply that the trend regarding the epidemic has attained a stable state and approached zero confirmed cases. Thus, China, Japan, Korea, and Australia seem to have recovered from the attack of COVID-19, while Brazil, Saudi Arabia, South Africa, and the USA remain engaged in the battle against COVID-19 and are required to devote more effort to create new opportunities. On 27 June 2020, China, Japan, Korea, and Australia had 24, 100, 51, and 37 daily new confirmed cases [ 1 ], respectively, being much lower than the peak of daily new confirmed cases for each country. In contrast, at the end of June 2020, Brazil and the USA continually set new records for daily new confirmed cases. The number of newly confirmed cases on 27 June 2020 was 39,483, 3938, 6215, and 40,526 cases for Brazil, Saudi Arabia, South Africa, and the USA, respectively [ 1 ]. On 30 June 2020, the European Council announced the easing of travel restrictions from 1 July 2020 for residents of recommended countries, including Australia, Japan, Korea, China, and Canada [ 38 ]. As indicated in Table 4, China, Japan, Korea, and Australia ranked first to fourth in epidemic stability. Canada was slightly behind in 13th place. To examine the appropriateness of lifting the travel restrictions at the external borders for residents of these countries, we used the data for 27 June 2020 as an example. On that day, the number of newly confirmed cases in China, Japan, Korea, Australia, and Canada was 24, 100, 51, 37, and 380, respectively, equivalent to a stability of 0.0168, 0.791, 0.995, 1.451, and 10.068 cases per million per day. The ES on 27 June 2020 in China, Japan, Korea, and Australia was much lower than the value of Germany’s ES (Table 4). This implies that the spread of COVID-19 had been controlled in these countries and was more stable than in Germany. The ES value on 27 June 2020 for Canada was nearly the same as that for France, as indicated in Table 4. However, Canada showed a U-shaped pattern for the trend in efficiency ranks, and it is suggested that the EU wait and observe the efficiency trend and the newly confirmed cases for Canada. Thus, the results suggest that the lifting of travel restrictions for these countries, with the exception of Canada, is quite reasonable based on the indicator of epidemic stability and the trends in efficiency ranking presented in this paper. 128 Healthcare 2021,9, 362 as well as the mechanism of action in the elderly and those suffering from underlying health conditions [ 5 ]. Additionally, the sudden emergence of numerous strains worldwide, such as the B.1.1.7 (United Kingdom) and B.1.1.28 (South Africa) linages, added to their divergent mutations, the little known information about their severity, transmission, and resistance have all put the fate of the newly proposed treatments and vaccines at stake [ 6 , 7 ]. In addition to the frightening infection and deaths worldwide, COVID-19 has taken its toll on the global economy. With increasing infections and multiple forced lockdowns, many businesses, manufacturing companies, and organizations reduced their activities, sales, and overall productions, which, in turn, slowed down the global economy until it almost came into a “freeze” [ 8 ]. The global economic knockout has drastically affected healthcare workers, particularly the radiology department. With the increasing need of intensive care unit (ICU) beds, medications, and personal protective equipment (PPE) on one hand, and the reduction in admissions on the other hand due to fear of contracting the virus, hospitals are struggling to maintain their revenues [ 9 ]. Radiology departments worldwide are experiencing a significant drop in imaging volumes, especially screening services for breast and lung cancer, after the American College of Radiology (ACR) and Centers for Disease Control and Prevention (CDC) implemented several guidelines, some of which included postponing and rescheduling non-urgent patient visits [10]. As for Lebanon, a 10,452 Km 2 country located in the Middle East, the battle with controlling the viral spread is strenuous. With the first COVID-19 infection reported on 21 February 2020, Lebanon has faced many obstacles that hindered the ability to slow the spread of the virus among its inhabitants [ 11 ]. Despite the fact that Lebanon was not the pioneer in healthcare according to the WHO’s report in 2000, holding the rank 91 [ 12 ], the country underwent various changes, reforms, and advancements to be ranked as the 23rd country worldwide in 2018 according to Bloomberg’s Healthcare Efficiency Index [ 13 ]. However, according to the same index, the country has later on declined and ranked 48th in 2020 amid the pandemic, since Lebanon was less prepared for such a pandemic compared to other countries. Unfortunately, this efficient healthcare system is not free for its citizens. This has led many Lebanese people suffering from COVID-19 symptoms to skip performing necessary diagnostic tests and avoid hospital admission simply because they cannot afford the cost. In addition, in spite of having some of the most advanced hospitals, Lebanon, like many other countries, was not prepared for such pandemic due to the limited number of beds in the ICUs and the significant shortage in ventilators. Therefore, in spite of all efforts to “flatten the curve”, Lebanon has recorded an exponential drastic increase in the daily number of cases and deaths. The reason behind that was the massive explosion that occurred at the Port of Beirut on 4 August 2020. The explosion killed over 200 people, injured more than 6000 others, and left around 300,000 people homeless [ 14 ], causing chaos in hospitals as well as a spike in the reported COVID-19-positive cases. Following the explosion, various essential hospitals in the capital were completely destroyed, thus putting more weight on the medical staff, especially the radiology department. On top of that, due to political problems, Lebanon is suffering from a critically deteriorating economic crisis. The national currency, the Lebanese pound (LBP), is falling stiff against the United States dollar (USD). It lost about 80% of its value, thus causing a severe inflation in the country [ 15 ]. With inflation reaching terrifyingly high levels, Lebanon is currently ranked second worldwide in terms of hyperinflation according to the Hanke’s Annual Inflation Rate model [ 16 ]. Besides increasing poverty level to 55% (compared to 28% in 2019) [ 17 ], this inflation negatively impacts the healthcare system as all products needed are imported in foreign currency (i.e., USD or EUR). Most healthcare institutions in the country are private hospitals and/or medical centers and laboratories. Therefore, there is a huge difficulty in coping with the increasing prices of materials and equipment, thus posing a risk to the staff’s health and safety as institutions administrators can no longer afford adequate and/or good quality PPE and disinfecting/cleaning agents. This increases the threat to healthcare members, especially radiographers, of contracting the virus from the workplace and transmitting it to their family and/or loved ones. The worsening eco135 Healthcare 2021,9, 362 nomic situation does not only strike the healthcare systems and staff in Lebanon financially, but also drains them of all energy and hospital beds by escalating the daily number of COVID-19-reported cases. Lebanese people are forced to break all lockdown rules and open their shops/businesses in order to put food on their tables and feed their families, a phenomenon accompanied by the absence of social distancing and precautions, thus reflecting a soar in COVID-19 cases and more pressure on the healthcare system. Following international guidelines, thoracic imaging, especially chest radiography and chest Computed Tomography (CT), are being laboriously used in all hospitals and imaging centers in Lebanon as powerful tools for the diagnosis, detection of complications, and follow-up of COVID-19 patients [ 18 ]. Various studies have proven the importance of chest CT in detecting SARS-CoV-2 in patients with negative reverse transcription polymerase chain reaction (RT-PCR) results [ 19 ]. One study involving 1014 patients showed that chest CT scan had a higher sensitivity (97%) compared to RT-PCR [ 20 ]. In addition, a recent study proved the practicability of magnetic resonance imaging (MRI) in the detection of pulmonary changes and damage caused by COVID-19, thus proposing a potential radiation-free alternative to chest CT, especially when periodic, repetitive scans are required for follow-up [ 21 ]. Furthermore, the newly emerging artificial intelligence (AI)-based algorithms that have the ability to detect COVID-19 pneumonia on chest CT with approximately 90.8% accuracy, 84% sensitivity, and 93% specificity [ 22 ], can consequently enhance the paramount role radiographers and medical imaging play during the COVID-19 pandemic. The huge workload on radiology departments in Lebanon, specifically on Lebanese radiographers or radiologic technologists, due to the ongoing pandemic, as well as the worsening economic situation, have led to many adverse effects such as irregular and disturbed shifts, loss of work, increased radiation exposure, deteriorating mental and physical health. There are currently no studies conducted in the region to point out the critical situation radiographers or radiologic technologists are going through. This study aimed to shed light on what the frontline heroes are going through in the midst of all these unprecedented crises, and evaluate factors associated with stress from contracting the COVID-19 virus from the workplace among radiography technicians. 2. Methods 2.1. Study Design A cross-sectional study was conducted among radiographers or radiologic technologists registered in the Lebanese Society of Radiographers (LSR) in multiple hospitals and medical centers all over the country. They were requested to fill out an electronic survey specifically tailored to inquire how the pandemic affected them, their work, and their overall wellbeing, directly and indirectly. The study was conducted from 3 December 2020 until 17 December 2020. 2.2. Minimal Sample Size Calculation On the basis of a population size of 325 active radiography technicians, and a 75.4% expected frequency of workplace-related stress after the outbreak [ 23 ], we found that the minimal sample size needed for bivariate and multivariable analysis was 152 according to the Epi-info software (Centers for Disease Control and Prevention, Atlanta, GA, USA) [ 24 ]. 2.3. Questionnaire and Variables The online survey, proposed in 3 languages (English, Arabic, and French), was distributed to all LSR registered members via the social network platforms (i.e., official WhatsApp groups of the syndicate). The survey was composed of 26 questions organized into 6 sections (general, workplace conditions, health and safety, mental/psychologic, financial, and skill/knowledge development questions). The first section aimed to study the demographical and educational status of the radiographers: age, gender, marital status, degree, and workplace type. The second section aimed to assess the changes made in 136 Healthcare 2021,9, 362 the workplace: variability in shifts, overall changes in department workload, workflow, protocols, and safety measures (PPE use, disinfection, compulsory mask use). The next section discussed the impact of the virus on technologists’ health and safety with questions evaluating whether they have contracted the virus, its severity, its transmission, and the need for any hospital admission. The following section analyzed the mental or psychologic outcome, with questions inquiring about the presence and severity of workplace-related stress, its impact on them and their family/loved ones, and the support received. The fifth section aimed to determine the financial impact of the pandemic on Lebanese radiographers by including questions concerning the monthly salary, the extent of modifications done to that salary, and whether the radiographers are considering quitting their jobs. The last section estimated a rather positive impact of the pandemic, especially during the lockdown periods and decreased shifts, in terms of skills and/or knowledge development with questions evaluating the use of free time in beneficial, recreational activities and the preferred type of activities. 2.4. Statistical Analysis Descriptive, bivariate, and multivariable statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS) v.25 (Armonk, NY, USA). The quantitative variables were expressed as percentages and comparisons were made using the chi-squared test. A multinomial regression was conducted, taking the stress/worry about contracting COVID-19 from the workplace categories (strongly disagree/disagree, agree/strongly agree, and neutral) as the dependent variable. The neutral group was taken as reference. The Nagelkerke pseudo R 2 values were also calculated to determine the variance explained by each independent variable of the outcome variable. Significance was set at p< 0.05. 3. Results A total of 212 survey responses that accounted for 32.5% of overall registered radiologic technologists or 65.3% of active members was received. Out of the three survey languages available, the Arabic language was preferred by almost 46.23% (n= 98) of radiographers, then the English language with 39.62% (n= 84) submissions, followed by the French language with 14.15% (n= 30) submissions (Figure 1). As for the rest of the survey questions, the results of the three survey languages were combined. Figure 1. Pie chart showing the percentage distribution of the three languages: English, Arabic, and French (the percentages were rounded to the nearest whole number). 3.1. General Questions Responses were received mainly from radiographers that belonged to the 20–29 age group (47.17%) and the 30–39 age group (31.13%), while only 1.89% of radiographers were 137 Healthcare 2021,9, 362 above 60 years old. Concerning the gender distribution of radiographers, responses were almost equally distributed between males and females, with the percentage of males being slightly greater than that of females (51.42% vs. 48.58%, respectively). As for the marital status, results showed that 52.36% of participants were married, 38.21% were single, 8.02% were engaged, and only 1.41% were divorced. Concerning the highest degree in the field, participants held a T.S./L.T. (technical degrees) in Radiography with 36.32% submissions, 16.98% had a university diploma, 33.96% earned their Bachelor of Science (B.S.) degree, 8.02% had a Master of Science (M.S.) degree, and only 4.72% chose the “other” option and relied mainly on practical experience. Regarding the distribution of work locations, most of the participants (71.23%) worked in private hospitals, 14.62% in imaging centers, and only 10.83% in public hospitals. Table 1 summarizes the questions, answers, and percentages for this section. Table 1. Table summarizing the questions, choices, and percentages concerning the general questions section. Question Choices and Percentages Age 20–29 47.17% 30–39 31.13% 40–49 11.79% 50–59 8.02% 60+ 1.89% Gender Males 51.42% Females 48.58% Marital Status Single 38.21% Engaged 8.02% Married 52.36% Divorced 1.41% Highest Degree T.S./L.T. 36.32% Diploma 16.98% B.S. 33.96% M.S. 8.02% Others 4.72% Work Location Private Hospital 71.23% Public Hospital 10.85% Lab/Medical Imaging Center 14.62% Others 3.30% 3.2. Workplace Conditions during the Pandemic A total of 69.81% of participants agreed that the workload in the department was affected by the pandemic (agree (45.28%), strongly agree (24.53%)). Similarly, the highest percentage of participants (58.49%) agreed (agree (37.74%), strongly agree (20.75%)) that their shift duration and distribution were impacted by the pandemic. While voting for the modality that received the most workload, the most selected choices were CT and X-ray, with 47.87% and 38.53%, respectively. Participants were given the chance to select more than one option resulting in a total of 353 votes, 169 for CT and 136 for X-ray. When asked whether the institution is applying an adapted safety protocol for COVID-19 patients, 67.45% of votes agreed (agree (47.17%), strongly agree (20.28%)). In the same sense, most radiographers agreed that their institution is providing PPE and/or cleaning/disinfecting agents with around 69.81% of the votes (agree (43.87%), strongly agree (25.94%)). Likewise, 88.68% of the participants agreed (agree (32.08%), strongly agree (56.60%)) that their institution is forcing all patients, visitors, and staff to wear face masks. Figure 2 summarizes the questions, answers, and percentages for this section. 138 Healthcare 2021,9, 362 Figure 2. Pie charts summarizing the questions, choices, and percentages concerning the workplace conditions section. 3.3. Health and Safety Responses showed that 64.15% of radiographers disagreed (disagree (25.00%), strongly disagree (39.15%)) that their institution is providing regular/periodic, free PCR testing for the staff, while only 25.94% agreed. The highest percentage of participants 74.53% did not contract the virus. Out of the 12.26% radiographers who caught the virus, 61.54% got it from the workplace, 34.62% suffered from mild symptoms, and 92.31% were not admitted to the hospital. Only 30.77% of infected radiologic technologists transmitted the virus to family members/friends/colleagues, while 50.00% did not, and 19.23% were not sure. Table 2 summarizes the questions, answers, and percentages for this section. 139 Healthcare 2021,9, 362 Table 2. Table summarizing the questions, choices, and percentages concerning the health and safety section. Question Choices and Percentages My institution is providing regular/periodic, free PCR testing for staff. Strongly Disagree 39.15% Disagree 25.00% Neutral 9.91% Agree 16.04% Strongly Agree 9.90% Have you contracted the virus? Yes 12.26% No 74.53% I am not sure 13.21% Is it from the workplace? Yes 61.54% No 11.54% I am not sure 26.92% What was the severity of the disease? No symptoms 7.69% Mild symptoms 34.62% Moderate symptoms 30.77% Severe symptoms 26.92% Were you admitted to the hospital? Yes 8.02% No 25.94% Did you transmit the virus to any family member/friend/colleague? Yes 30.77% No 50.00% I am not sure 19.23% 3.4. Financial Questions Participants were asked to give an estimate (in LBP) of their original monthly salary provided by the institution, according to the work contract (Figure 3). Figure 3. Bar graph presenting the percentages of salary ranges as disclosed by the participants. While 60.85% of radiologic technologists had no change in their salary, 30.19% disclosed that they had 25–50% or even more than 50% reductions in their salary, whereas 4.24% were not getting paid their monthly salary. Moreover, 61.11% of participants disagreed (disagree (36.42%), strongly disagree (24.69%)) on leaving their job/staying home, while 35.80% had a different opinion. Table 3 summarizes the questions, answers, and percentages for this section. 140 Healthcare 2021,9, 362 Table 3. Table summarizing the questions, choices, and percentages concerning the financial questions section. Question Choices and Percentages To what extent did the institution modify/decrease the monthly salary provided to you in accordance with the economic situation? Severely (>50% reduction) 6.13% Moderately (25–50% reduction) 24.06% No Change 60.85% My salary was modified but what was reduced will be paid later on 4.72% I am not getting paid my monthly salary 4.24% I am considering leaving job/staying home, as it is not worth it. Strongly Disagree 24.69% Disagree 36.42% Neutral 3.09% Agree 25.31% Strongly Agree 10.49% 3.5. Mental/Psychological Questions Concerning mental/psychological questions, 60.85% of radiographers agreed (agree (35.85%), strongly agree (25.00%)) that they were feeling stressed/worried about contracting the virus from the workplace. Similarly, 67.92% agreed (agree (45.75%), strongly agree (22.17%)) that their family members, friends, and/or loved ones were affected by this workrelated stress. Furthermore, 43.86% of surveyors disagreed (disagree (20.75%), strongly disagree (23.11%)) that their institution is showing adequate social, psychological, and/or financial care/follow up for staff members who contracted the virus. Half of the participants disagreed (disagree (25.47%), strongly disagree (24.53%)) about thinking/planning to change the field of work and leave the healthcare system; 69.81% of responders agreed (agree (25.00%), strongly agree (44.81%)) about leaving the country to seek a better opportunity abroad, while only 16.04% voted for the opposite. Figure 4 summarizes the questions, answers, and percentages for this section. 3.6. Skill/Knowledge Development More than half of the participants (65.09%) used their free time during the lockdown for skill development and/or knowledge expansion. Numerous options were selected regarding the kind of activities done, and many of the participants chose the “other” option. For simplicity, the kinds of activities done are summarized in the bar graph below (Figure 5). 3.7. Bivariate Analysis A significantly higher percentage of persons who had a neutral opinion about the workload being affected by the pandemic agreed/strongly agreed that they are stressed and worried about contracting COVID-19 from the workplace (Table 4). No significant association was found between all other variables and the stress/worry about contracting COVID-19 from the workplace. 141 Healthcare 2021,9, 362 Figure 4. Pie charts summarizing the questions, choices, and percentages concerning the mental/psychological questions section. Figure 5. Bar graph summarizing the various types of recreational activities done by the participants during their free time in the lockdown. 142 Healthcare 2021,9, 362 Table 4. Bivariate analysis of factors associated with stress categories. Variable Stress/Worry about Contracting COVID-19 from the Workplace p Neutral Strongly Disagree/Disagree Agree/Strongly Agree Age categories (in years) 0.145 20–29 27 (26.5%) 22 (21.6%) 53 (52.0%) 30–39 16 (24.2%) 9 (13.6%) 41 (62.1%) 40–49 4 (16.0%) 3 (12.0%) 18 (72.0%) 50 and above 1 (4.8%) 3 (14.3%) 17 (81.0%) Gender 0.238 Male 29 (26.6%) 20 (18.3%) 60 (55.0%) Female 19 (18.1%) 17 (16.2%) 69 (65.7%) Marital status 0.841 Single/engaged/divorced 25 (24.3%) 17 (16.5%) 61 (59.2%) Married 23 (20.7%) 20 (18.0%) 68 (61.3%) Workload affected by the pandemic 0.034 Strongly disagree/disagree 12 (29.3%) 6 (14.6%) 23 (56.1%) Neutral 0 (0%) 5 (21.7%) 18 (78.3%) Agree/strongly agree 36 (24.0%) 26 (17.3%) 88 (58.7%) Shift duration distribution impacted during the pandemic 0.204 Strongly disagree/disagree 12 (23.5%) 4 (7.8%) 35 (68.6%) Neutral 8 (21.6%) 5 (13.5%) 24 (64.9%) Agree/strongly agree 28 (22.2%) 28 (22.2%) 70 (55.6%) Institution applies adapted safety protocol 0.268 Strongly disagree/disagree 4 (15.4%) 2 (7.7%) 20 (76.9%) Neutral 8 (18.6%) 6 (14.0%) 29 (67.4%) Agree/strongly agree 36 (24.8%) 29 (20.0%) 80 (55.2%) Institution supplying cleaning agents 0.224 Strongly disagree/disagree 4 (17.4%) 4 (17.4%) 15 (65.2%) Neutral 6 (14.6%) 4 (9.8%) 31 (75.6%) Agree/strongly agree 38 (25.3%) 29 (19.3%) 83 (55.3%) Institution forces mask wearing 0.083 Strongly disagree/disagree 4 (36.4%) 0 (0%) 7 (63.6%) Neutral 0 (0%) 2 (15.4%) 11 (84.6%) Agree/strongly agree 44 (23.2%) 35 (18.4%) 111 (58.4%) Numbers in bold indicate significant p-values. 3.8. Multivariable Analysis The results of the regression, taking stress/worry about contracting COVID-19 from the workplace (agree/strongly disagree vs. neutral*) as the dependent variable, showed that having 50 or more years vs. 20–29 years (adjusted odds ratio (aOR) = 9.53; p= 0.036) was significantly associated with higher odds of agreeing/strongly agreeing about having stress/worry about contracting COVID-19 from the workplace (Table 5). Table 5. Multinomial regression. Stress/Worry About Contracting COVID-19 from the Workplace (Agree/Strongly Agree vs. Neutral) Variable aOR p95% CI Age categories (in years) 0.182 20–29 1 30–39 1.28 0.531 0.59–2.79 40–49 1.69 0.407 0.49–5.81 50 and above 9.53 0.036 1.16–78.30 Numbers in bold indicate significant p-values. 143 Healthcare 2021,9, 362 None of the variables were significantly associated with disagreeing/strongly disagreeing about having stress/worry about contracting COVID-19 from the workplace compared to neutral. Variables entered in the model: workload affected by the pandemic, institution forces mask wearing, age categories (Nagelkerke pseudo R 2 = 21.3%); Nagelkerke pseudo R 2 for the variable workload affected by the pandemic = 6.9%; Nagelkerke pseudo R 2 for the variable institution forces mask wearing = 6.4%; Nagelkerke pseudo R 2 for the variable age categories = 6.8%; numbers in bold indicate significant p-values (p< 0.05). 4. Discussion Radiology, especially chest X-ray and CT examinations, has played a crucial role and proven its effectiveness in the diagnosis and follow-up of pneumonia in COVID-19 patients, in addition to assessing better treatment protocols, including measurement of disease changes and predicting prognosis [ 25 ]. Radiologic technologists stand equal to, and side by side with, all doctors and nurses who are fighting hand-in-hand in this pandemic, deserving the title “frontline heroes”. With the radiology department being the primary destination to all Emergency Room (ER) patients suffering from respiratory problems and suspected to be COVID-19-positive, the radiographers’ roles and direct contact with these patients weigh no less than those of their medical colleagues. This distinctive study is the first of its kind in the region and aimed to assess the direct and indirect impact of the ongoing COVID-19 pandemic on radiographers in a country severely affected by the COVID-19 pandemic, in addition to political and economic crisis. Most radiologic technologists in the country are relatively young (belonging to the 20–29 age group), with the genders almost equally distributed between males and females. Institutions all over the country are facing a decrease in imaging volumes, as shown by the large number of radiographers who agreed/strongly agreed that the workload in their department and shifts were impacted. Similarly, many hospitals and imaging centers around the globe have also seen significant drops in non-urgent outpatient visits, imaging, and services, even below baseline values. Nevertheless, thoracic imaging volumes involving X-ray and CT were not severely impacted by the pandemic [ 26 ]. The results clearly show the vital role of X-ray and CT scan in this management, as they received the greatest amount of votes regarding the modalities, with most workload reflecting the important role of thoracic imaging in managing patients during the pandemic by detecting signs of COVID-19 pneumonia [ 27 ]. Most healthcare systems are implementing and adapted safety protocol when dealing with COVID-19 patients and are supplying the staff with the appropriate PPEs (i.e., masks, face shields, gloves, etc.) and cleaning/disinfecting agents. The strict rules concerning the compulsory use of face masks by patients, visitors, and staff also reflect the efficiency of institutions’ efforts in controlling disease spread, a vital strategy that is implemented around the globe for disease handling and protection of the staff from infections [ 28 ]. Proven effective, a very high number of participants did not contract the virus. Those who did, on the other hand, suffered from mild to moderate symptoms and did not require hospital admission. As for the financial aspect, the median monthly income of the Lebanese radiographers is 1661,125 LBP, which is relatively low compared to the high hyperinflation levels the country is suffering from and the fall of the Lebanese currency compared to foreign currencies, especially in black markets. The radiographer’s average monthly salary that used to be equivalent to around 1096 USD pre-hyperinflation (official exchange rate 1 USD = 1515 LBP) now barely equals 144 USD (black market 1 USD = 11,500 LBP). Despite the economic crisis, more than half of the participants reported no change in their monthly income, yet a considerable number of radiographers are suffering from a 20 to 50% reduction in their salary. A struggle that reflects the fact that the middle-income level of the Lebanese population is shrinking. Regardless of all challenges and reductions, radiographers disagreed quitting their job and/or staying home, as they strongly hold onto their humane role and life-saving duties at all costs. The proceeding pandemic has affected Lebanese radiographers, not only 144 Healthcare 2021,9, 220 awareness regarding the pandemic to use prevention measures to the contain pandemic. YouTube is considered to be the most inspiring way to share the coronavirus pandemic and improve community health services. Peyrav et al. [ 11 ] considered a case of Iran’s economy that has been negatively affected by the coronavirus, and the sizeable number of registered cases is increasing day-by-day. The Iranian government has worked dedicatedly to contain the coronavirus through massive public education programs and electronic awareness campaigns, while, on the other side, the country is conducting research workshops, training, and increasing healthcare budgets to the control the pandemic. The need for word-ofmouth campaigns regarding coronavirus prevention is vital to promote country resilience. Sahu [ 12 ] suggested several policy measures to contain the coronavirus among the students and teaching/administrative staff, as high risk is associated with the educational institutes through close contacts. As per WHO guidelines, the closure of all educational institutes is deemed to be desirable for such an indefinite period until the virus can be controlled accordingly. Nevertheless, these positive measures negatively impact the mental health of students and academic staff. Thus, the need for proper counselling, online teaching courses, assessments, and evaluation is highly desirable in improving students and academic staff’s psychological health. Bhalekar [ 13 ] argued that the Indian economy was mainly affected by the coronavirus pandemic due to a random lockdown in a country that increases the daily wagers’ miseries, which led to increasing the source of virus in a country. The COVID-19 affects all major sectors of the Indian economy, not limited to the labor market, educational institutes, electronic commerce, and overall economic growth. The high need for strategic thinking, unified global policies, smart lockdowns, emergency relief packages to the poor laborer, and stable financial markets would help control to the coronavirus resourcefully. Zheng [ 14 ] suggested the need for psychological treatment of healthcare workers directly exposed to the coronavirus, and they are more likely concerned about their families and friends. This family support for health care workers is desirable for working with sound health. Sanitàdi Toppi et al. [ 15 ] confined their findings on a more important aspect of spreading coronavirus pandemic related to the airborne particulate, providing a channel to carry coronavirus into the human respiratory system. The urgent need is for making sustainable policies to limit particulate matters and limit the virus accordingly. Musselwhite et al. [ 16 ] suggested that public transportation could be a carrier of coronavirus spread, as people are either sitting or standing in a closed environment, while coughing, touching, and sneezing may transmit the microorganism from one to others. The doors, ticket machines, windows, elevators, seats, and many other areas could be possible places of the infectious disease. Deng and Peng [ 17 ] found that the case-fatality ratio is mainly evident with the following symptoms of coronavirus, including high fever, too much coughing, shortness of breath, and chest pain, while other comorbidities of the fatality cases, including high stress, heart patients, diabetes, cerebral infarction, and chronic bronchitis. These healthcare concerns that are required more good policies to reduce the case fatality ratio, while symptomatic treatment is provided to the coronavirus patients until the possible medication and the vaccine is not invented. Anser et al. [18] considered a panel of 76 countries using time series data from 2010–2019 to evaluate the impact of COVID-19 measures on global poverty, and found that population density, lack of necessary sanitation facilities, environmental challenges, and death by communicable diseases put a significant burden on the low-income group, which could be minimized by increasing public healthcare expenditures across countries. The need for pro-poor growth policies will support the breakdown of the vicious cycle of poverty that would be further translated into sustained economic growth. The significant discussion that is based on earlier literature emphasized the need to evaluate the possible impacts of COVID-19 measures on services industries while using an aggregated world data level. Fewer studies on the stated topic give room to investigate to select the specified area, which would provide more policy insights for analyzing the services industry’s response against the COVID-19 measures. The objectives of the study are as follows: 151 Healthcare 2021,9, 220 (I) To examine the possible impacts of COVID-19 measures on the global services industry. (II) To investigate the direct effect of communicable diseases, including COVID-19, on services value-added. (III) To determine the role of word-of-mouth against the coronavirus pandemic and its possible impact on the services industry and (IV) To observe the effects of lockdown, social distancing, price control, and financial activities on the services industry. Different countries have widely adopted these measures against the coronavirus that analyzed in the study of the services sector. The study used quantile regression estimates to analyze the predictors’ different variations on the response variable at different quantiles distribution. This technique is better in a given scenario that will provide robust inferences. 2. Data Sources and Methodological Framework There are some COVID-19 measures that have been used to control the pandemic at a global scale, and a few of them are listed below, i.e., (I) Information Sharing: the right information with correct facts and figures are the responsibility of every government to share with their residents, while, at an international platform, the WHO and other international agencies have to prepare the policy documents regarding prevention from novel coronavirus and spread it through different information channels. The national and international agencies have already provided the right information through various communication channels, and it is now a duty to respond to the general masses to act like a civilized person. The ‘word-of-mouth’ mostly used the word in marketing the specified products where information is shared from one person to another through oral communication [ 19 ]. The adult literacy rate played a vital role in promoting communication channels to reach the right customers. Based on the above discussion, this study used the adult literacy rate (% of people ages 15 and above) as a correct variable for information sharing about coronavirus, and considers it as word-of-mouth (as denoted by WOM) information novel coronavirus among the general masses in this study. The rationale for using this proxy is that the literate person would effectively use all kinds of communication among agents. Hence, this proxy would leave the impact on the literature of network awareness. (II) Lockdown: the lockdowns, either partial or complete, depend upon the severity of the new coronavirus outbreak in any country. This strategy used almost every country in their perspectives to prevent their ordinary peoples from the deadly disease. The evidence indicates that lockdown is not successful in many parts of the world due to the high incidence of poverty and hunger, which were later funded by the government’s emergency reliefs’ packages for the needy peoples [ 20 ]. The law enforcement agencies played an essential role in lockdown in the city, as per Federal government instructions [ 21 ]. The people did not usually follow the government instructions due to ignorance, a lack of information, and other social issues; for this call, law enforcement agencies can handle this situation. Thus, this study used ‘armed forces personnel’ (in total) for a nearby LOCKDOWN proxy to restrict free mobility. Measuring “lockdowns” by armed forces personnel is used to show a stringent government policy that, using ‘power and control’ to contain widespread coronavirus cases by the forceful imposition of standard operating procedures (SOPs) regarding coronavirus prevention, likely shows a better proposition than the ‘Oxford stringency index’ or ‘Google mobility series’. (III) Social Distancing: according to the WHO guidelines regarding preventing and controlling the coronavirus pandemic, it avoids massive gatherings and maintains physical distancing among the residents. This strategy is mostly applied uniformly across the globe. Physical distancing helps to minimize the risk of coronavirus incidence as it is a transmitted disease, and its spread from close contacts [ 22 ]. The population compactness could be one reason that provides a channel to carry one person to another [ 18 ]. This study used ‘population density’, as per square km of land area, as a nearby proxy for the so152 Healthcare 2021,9, 220 cial distancing (denoted by SOCDIS) to obtain some conclusive findings in this regard. The study measures “social distancing” by population density, rather than using % of the urban population because, the higher the population compact in the country, the greater will be the chances to spread coronavirus cases, irrespective of rural and urban spheres. The study did not limit population density to the urban population while it used the overall population compactness, as coronavirus cases are spreading uniformly in rural and urban regions. (IV) Price Control: due to the coronavirus outbreak, the globalized world’s most critical concern is the ‘price control’ of the food items especially. As the news about the COVID-19 outbreak transmitted across the globe, mass panic spread among ordinary people, and they rushed at food items to store in their homes. Every time, the governments give confidence to the familiar people and the producers and retailers to keep calm and remain easy so that food challenges can be resolved. In this regard, governments make food control price committees in a different part of the world to provide a free flow of food supply at lower prices [ 23 ]. The present study used the ‘consumer price index-inflation’ (%) as a proxy of food price control in the sense that the coronavirus pandemic increases the prices of food items due to the shortage of the food supply chain. Thus, the need to assess the price hikes can be used through CPI values for making an effective price control strategy. (V) Financial and Economic Activities: the outbreak of novel coronavirus negatively affects the global stock market index. It is crushed in many parts of the world, due to full travel restrictions, lockdowns, and other preventive measures, which directly hit the local and international businesses [ 24 ]. The study used ‘broad money supply’, as % of GDP and ‘GDP per capita’ in constant 2010 US$ as nearby proxies of financial activities (denoted by FACT) and economic activities (EACT), respectively, to assess the country’s economic and financial situation amid the coronavirus pandemic. The rationale to use both of the factors is that money supply is considered to be one of the vital factors of financial development indicators that mostly viewed in the relation of COVID-19 pandemic. Similarly, economic activities can be better checked with the country’s per capita income that mainly affected the pandemic recession. Thus, these proxies would be helpful in tracing the real problem of the pandemic recession across countries. (VI) Causes of Death by Communicable Diseases: the study used the data of ‘causes of death by communicable diseases’ (as % of total) (denoted by COMD) as a reference point to analyze the death toll by a coronavirus. (VII) Services Value Added: the service’s value added (% of GDP) (as denoted by SVAD) comprises distributive services, producer services, personal services, and social services, which is used in this study as a response variable. The COVID-19 measures are considered to be explanatory variables of the study, while the value of the service added is served as the explained variable. The world aggregated data are used for empirical analysis, covering a period of 1975–2020. The missing information is filled by the preceding and succeeding value of the respective variables where required. The data were obtained from the World Bank [25]. The study benefited from the Keynesian theory of aggregate demand, which argued that aggregate demand could be affected by any prevailing shocks in the economies, which need to be stabilized through economic policies. Similarly, the COVID-19 crisis is more severe than the financial depression of 2008 shocks that captured the whole world, which declined world economic growth. The COVID-19 crisis has affected the economies’ supply and demand simultaneously; for instance, social distancing creates distancing between the one person and another. It reduces productive labor hours that lower the supply, which increases the marginal cost of production [ 26 , 27 ]. Significantly, the services sector is majorly affected through social distancing, as its closely connected with the hospitality and recreational activities that are banned due to the high risk of spreading COVID-19 cases. Services value-added is a substantial part of economic growth, as its GDP share is more than 60% worldwide. The services sector is used as a reference point that 153 Healthcare 2021,9, 220 analyzed its performance in the world’s GDP that is most affected by COVID-19 pandemic, which can be viewed by the suggested empirical equation, i.e., ln(SVAD)=α0+α1ln(COMD)+α2ln(WOM)+α3ln(LOCKDOWN)+α4ln(FACT)+α5ln(EACT) +α6ln(PCONT)+α7ln(SOCDIS)+ε ∴∂ln(SVAD) ∂ln(COMD)<0, ∂ln(SVAD) ∂ln(WOM)>0, ∂ln(SVAD) ∂ln(LOCKDOWN)<0, ∂ln(SVAD) ∂ln(FACT)<0, ∂ln(SVAD) ∂ln(EACT)<0, ∂ln(SVAD) ∂ln(PCONT)<0, ∂ln(SVAD) ∂ln(SOCDIS)<0. (1) where SVAD shows the services value-added, COMD shows communicable diseases, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activities, EACT shows economic activities, PCONT shows price control, SOCDIS shows social distancing, and εshows the error term. Equation (1) shows that the stated factors influence service value-added. It is likely that the causes of death by communicable diseases, including COVID-19, will decrease service value-added, whereas improving the communication means of information sharing, including word-of-mouth about coronavirus pandemic, would be helpful in maintaining service value-added share relative to GDP. Although it is not favorable to the value of the functions added in managing their GDP share, the temporary or complete lockdown is not favorable. However, it is deemed to be desirable to control coronavirus on a global scale. The financial and economic activities suppressed with the COVID-19 pandemic negatively influenced services value-added. The price hikes in food items needed efficient price control to facilitate the needy community members, which subsidized the services sector to charge a smaller price, thus maintaining reasonable profit. Finally, social distancing is the remedial measure to contain coronavirus; however, it negatively affects service value-added. Figure 1 shows the research framework of the study. Figure 1 shows the impacts of COVID-19 measures on the services industry and identified some significant determinants that negatively affect global service value added. These COVID-19 measures are highly required for the controlled pandemic; however, it decreases services share relative to its country GDP. The following research hypotheses have been developed to analyze it during estimation, i.e., Hypothesis 1 (H1). Communicable diseases, including COVID-19, will likely decrease the share of services value-added relative to the country’s GDP. Hypothesis 2 (H2). Word-of-mouth of coronavirus pandemic would likely to be helpful for the prevention of virus and increases services value-added, and Hypothesis 3 (H3). Lockdown, population compactness, and financial instability will likely decrease services share in total GDP. The study utilized a quantile regression apparatus to obtain parameter estimates. It works under different assumptions. It gives a more trending analysis of the said parameters at different quantiles distribution, which other available regression apparatuses would be powerless to perform, such as time-series cointegration techniques, instrumental regression techniques, and robust regression. These techniques would perform well in their domain, but these are ineffective in analyzing trending regression estimates over 10th quantiles to 90th quantiles. The given procedure would give greater leverage to express the parameter estimates for sound inferences. Equation (2) shows the empirical illustration of different quantiles distribution of the stated parameters for ready reference, i.e., 154 Healthcare 2021,9, 220 ln (SVAD)τ10 =α0+α1ln (COMD)τ10 +α2ln (WOM)τ10 +α3ln (LOCKDOWN)τ10 +α4ln (FACT)τ10 +α5ln (EACT)τ10 +α6ln (PCONT)τ10 +α7ln (SOCDIS)τ10 +ετ10 ; ln (SVAD)τ25 =α0+α1ln (COMD)τ25 +α2ln (WOM)τ25 +α3ln (LOCKDOWN)τ25 +α4ln (FACT)τ25 +α5ln (EACT)τ25 +α6ln (PCONT)τ25 +α7ln (SOCDIS)τ25 +ετ25 : ln (SVAD)τ50 =α0+α1ln (COMD)τ50 +α2ln (WOM)τ50 +α3ln (LOCKDOWN)τ50 +α4ln (FACT)τ50 +α5ln (EACT)τ50 +α6ln (PCONT)τ50 +α7ln (SOCDIS)τ50 +ετ50 : ln (SVAD)τ75 =α0+α1ln (COMD)τ75 +α2ln (WOM)τ75 +α3ln (LOCKDOWN)τ75 +α4ln (FACT)τ75 +α5ln (EACT)τ75 +α6ln (PCONT)τ75 +α7ln (SOCDIS)τ75 +ετ75 : ln (SVAD)τ90 =α0+α1ln (COMD)τ90 +α2ln (WOM)τ90 +α3ln (LOCKDOWN)τ90 +α4ln (FACT)τ90 +α5ln (EACT)τ90 +α6ln (PCONT)τ90 +α7ln (SOCDIS)τ90 +ετ90 (2) where τ10 to τ90 show quantiles regression estimates from 10th quantiles to 90th quantile distribution. Figure 1. Research Framework of the Study. Source: Author’s extract. 155 Healthcare 2021,9, 220 The study further used impulse response function (IRF) and variance decomposition analysis (VDA) for analyzing the parameter estimates in the forecasting framework for the next ten year time period. 3. Results Table 2 shows the descriptive statistics of the candidate variables. The share of service value that is added to world GDP has reached a maximum of 65.26%, minimum at 54.24%, and mean 58.47%. The causes of death by communicable diseases, on average, are entered at 27.29% of total world death. Word-of-mouth is measured by an adult literacy rate with a minimum value of 65.19%, a maximum amount of 87.30%, and an average value of 77.30%. Armed forces personnel are used as a proxy for lockdown, which shows that the global world needed 25,744,314 armed forces personnel to keep successful lockdown to some specified area on average. The financial and economic activities are measured by broad money supply and GDP per capita, with an average value of 92.75% of GDP and US$8,007.36, respectively. The price control is measured by changes in the price level with an average value of 6.39%. Finally, social distancing is observed by population compactness, which has an average value of 45.95 people per square m of land area. The given descriptions of the candidate variables showed a trend analysis over the past 45 years. Table 2. Descriptive Statistics. Methods SVAD COMD WOM LOCKDOWN FACT EACT PCONT SOCDIS Mean 58.47710 27.29407 77.30327 25744314 92.75388 8007.362 6.394944 45.95990 Maximum 65.26177 30.90569 87.30101 30196640 125.0989 10892.00 12.47161 59.63624 Minimum 54.24299 20.17717 65.19396 22209230 62.15986 5681.743 1.431611 31.91508 Std. Dev. 4.384618 4.428943 7.039300 2934139 18.24449 1571.258 3.560119 8.506539 Skewness 0.212882 −0.521528 −0.341577 −0.087964 −0.073547 0.346740 0.464993 −0.013921 Kurtosis 1.326914 1.512059 1.738246 1.402185 2.251956 1.834101 1.970756 1.779506 Source: World Bank [ 25 ]. Note: SVAD shows services value-added, COMD shows communicable disease, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activity, EACT shows economic activity, PCONT shows price control, and SOCIDIS shows social distancing. Table 3 shows the correlation estimates and found that communicable diseases, including COVID − 19 and price control, negatively correlate with service value-added. In contrast, the other variables, including word-of-mouth, lockdown, financial and economic activities, and social distancing, positively associate services value-added. The result implies that services value-added exposed an increased risk of coronavirus pandemic, while strict price control further decreases services value-added across the globe. The government’s measures to controlled coronavirus would be primarily supported services value added to run their businesses during a relaxed time as per governments’ provision to open their markets. The word-of-mouth for coronavirus pandemic to the general masses would help keep residents at their homes to become safe from the virus, while, for successful operating lockdowns, the increasing number of armed forces personnel is desirable. Financial and economic activities allow general masses to start their businesses under strict government safety measures at their business site. Finally, social distancing is the only step that helps the broad population to keep away from the coronavirus; thus, avoiding massive gatherings and close contacts would enable peoples to do their work under the safety parameters. All of this positivity would support services value-added on a global scale. Table 4 shows the ADF unit root estimates and found that, except SOCDIS, the remaining variables exhibit the first difference stationary, while SOCDIS does not show either I(0) or I(1) characteristics, thus it does not confirm the order of integration at the level or first difference. Based on the estimates, the study moves towards quantile regression estimates to show the variations of variables at different quantiles distribution. 156 Healthcare 2021,9, 220 Table 3. Correlation Matrix. Variables SVAD COMD WOM LOCKDOWN FACT EACT PCONT SOCDIS SVAD 1 —– COMD −0.925 1 (0.000) —– WOM 0.912 −0.834 1 (0.000) (0.000) —– LOCKDOWN 0.726 −0.543 0.821 1 (0.000) (0.000) (0.000) —– FACT 0.857 −0.809 0.953 0.708 1 (0.000) (0.000) (0.000) (0.000) —– EACT 0.946 −0.927 0.955 0.703 0.943 1 (0.000) (0.000) (0.000) (0.000) (0.000) —– PCONT −0.832 0.734 −0.894 −0.716 −0.887 −0.832 1 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) —– SOCDIS 0.931 −0.887 0.987 0.772 0.964 0.986 −0.875 1 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) —– Note: Small bracket shows probability value. SVAD shows services value-added, COMD shows communicable disease, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activity, EACT shows economic activity, PCONT shows price control, and SOCIDIS shows social distancing. Table 4. Unit Root Estimates. Variables Level First Difference Constant Constant with Trend Constant Constant with Trend SVAD −0.062 (0.947) −2.152 (0.503) −5.892 (0.000) −5.879 (0.000) COMD −0.264 (0.921) −2.196 (0.479) −6.178 (0.000) −6.741 (0.000) WOM −1.542 (0.503) −0.631 (0.971) −4.663 (0.000) −4.922 (0.001) LOCKDOWN −1.617 (0.465) −2.073 (0.545) −7.663 (0.000) −7.579 (0.000) FACT −0.386 (0.902) −2.016 (0.576) −5.932 (0.000) −5.859 (0.000) EACT 1.214(0.997) −1.332 (0.866) −5.028 (0.000) −5.255 (0.000) PCONT −1.757 (0.396) −3.107 (0.117) −7.776 (0.000) −7.718 (0.000) SOCDIS −0.972 (0.754) −0.930 (0.943) −0.730 (0.828) −0.147 (0.992) Note: small bracket shows probability values. SVAD shows services value-added, COMD shows communicable disease, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activity, EACT shows economic activity, PCONT shows price control, and SOCIDIS shows social distancing. Table 5 shows the quantile regression estimates and found that communicable diseases hurt the services value added at different quantiles distribution with a minimum impact of − 0.068% and maximum impact of − 0312%, while an increasing one per cent increase in services value-added share to the globe GDP. The results are interpreted in light of the novel coronavirus. Kim et al. [ 28 ] argued that community health is mainly influenced by the coronavirus outbreak that has increased the healthcare burden in national healthcare 157 Healthcare 2021,9, 220 bills. The case study of New York city developed some protocols for the outpatient service department to minimize the risk of coronavirus pandemic, including a first stage, the possible test of coronavirus is performed on the susceptible patients. If found to be positive, then the second step is to give symptomatic treatments. The third stage is to track the patients once during at least five consecutive days, and, finally, teach them how to isolate in-home or elsewhere under prescribed medical guidelines. Samarathunga [ 29 ] discussed the possible challenges of the coronavirus pandemic on international tourism in Sri Lanka. The results show that the coronavirus pandemic negatively affects the country’s tourism sector, as it adversely affects the source markets, local tourism resources, and travel industry. The suspension of transportation modes, partial and complete lockdowns, and maintaining the distance between humans all decline tourism income. However, these measures are essential in containing coronavirus in a country. Yang et al. [ 30 ] concluded that, due to the high health risk of coronavirus pandemics to the national and international tourists, the tourism demand decreases through government institute bans on human mobility. Further, travel restrictions that are imposed by the government exacerbate adverse outcomes from the tourism sector. Thus, social welfare is the subject matter and prime responsibility of the government. Any strict policies regarding their prevention are desirable. However, the governments should subsidize the tourism sector to improve tourism sites; once the pandemic vanishes, an enormous amount of tourism revenue could be generated. Wanjala [ 31 ] argued that novel coronavirus negatively affects a country’s economic growth via low international trade and tourism transmission mechanisms. The travel and transportation restrictions for possible caution to take care of the humans from coronavirus are desirable, being substituted by the specific government-initiated reforms packages to the tourism and trade to maintain economic activities countrywide. Table 5. Quantile Regression Estimates. Quantiles τ10 τ20 τ30 τ40 τ50 τ60 τ70 τ80 τ90 LOG(COMD) −0.310 −0.313 −0.283 −0.262 −0.115 −0.105 −0.084 −0.068 −0.162 (0) (0) (0) (0.012) (0.008) (0.026) (0.064) (0.158) (0.017) LOG(WOM) 0.699 0.860 0.711 0.750 0.327 0.243 0.170 0.201 0.343 (0.001) (0.001) (0.006) (0.022) (0.605) (0.705) (0.794) (0.777) (0.704) LOG(LOCKDOWN) 0.027 0.010 0.043 0.056 0.247 0.239 0.231 0.208 0.243 (0.484) (0.823) (0.470) (0.472) (0.011) (0.028) (0.051) (0.114) (0.137) LOG(FACT) −0.112 −0.138 −0.127 −0.129 −0.072 −0.097 −0.122 −0.155 −0.118 (0) (0) (0.001) (0.008) (0.350) (0.289) (0.234) (0.201) (0.405) LOG(EACT) 0.220 0.178 0.165 0.222 0.418 0.392 0.397 0.420 0.261 (0.026) (0.118) (0.208) (0.181) (0.031) (0.037) (0.027) (0.030) (0.218) LOG(PCONT) −0.027 −0.029 −0.032 −0.032 −0.030 −0.030 −0.026 −0.027 −0.022 (0.002) (0.008) (0.010) (0.037) (0.009) (0.005) (0.017) (0.023) (0.171) LOG(SOCDIS) −0.442 −0.451 −0.374 −0.439 −0.447 −0.346 −0.252 −0.239 −0.256 (0.008) (0.018) (0.062) (0.083) (0.282) (0.354) (0.480) (0.518) (0.557) Constant 1.834 1.959 1.741 1.020 −2.875 −2.435 −2.343 −2.239 −1.839 (0.170) (0.212) (0.366) (0.678) (0.071) (0.076) (0.034) (0.041) (0.100) Statistical Tests Slope Equality Test Wald Test χ2–statistic: 37.055 χ2–statistic degree of freedom = 14 Probability value: 0.000 Symmetric Quantiles Test Wald Test χ2–statistic: 13.616 χ2–statistic degree of freedom = 8 Probability value: 0.092 Note: Small bracket shows probability value. SVAD shows services value-added, COMD shows communicable disease, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activity, EACT shows economic activity, PCONT shows price control, and SOCIDIS shows social distancing. 158 Healthcare 2021,9, 220 The sound financial activities, price control measures, and social distancing have proven to be the best strategy to control coronavirus; however, these measures negatively impact services value-added, leading to a global depression. The positive impact of wordof-mouth, lockdown, and sound economic activities decreases the risk of coronavirus pandemic and supports the services share into the world GDP. These results have been shown at different quantiles distribution. Brodeur et al. [ 32 ] discussed the vulnerability of the COVID-19 pandemic at a mass scale across the globe. The government put many efforts to restrain coronavirus through multiple strategies. However, the unified adopted policy included lockdown, which bears multifaceted mental health challenges to population well-being that are not limited to boredom, loneliness, sadness, worry, suicidal thoughts, stress, and divorce. The need for smart lockdowns and information sharing among the masses to stay safe in homes would be desirable, while the government should engage their population in some online group tasks to reduce mental health challenges. Wong [ 33 ] described the real situation of the coronavirus pandemic in the Malaysian context, where the physical distancing along with the national lockdowns were enforced with the one order command that local population from international travels, not allowing foreigners to visit a country, temporary shut down of businesses, closure of schools, colleges, and other institutions. At the same time, only essential services have been permitted under safety measures. These measures affect industries, including the services industry, which may cause a global depression. Barro et al. [ 34 ] found that the coronavirus pandemic and Spanish flu increase mortality and economic contraction, mostly low real returns on stocks and short-term government bills. Gómez-Ríos et al. [ 35 ] concluded that the coronavirus pandemic was mainly out of control due to the imported number of cases from uncontrolled air travellers. Social distancing avoids massive gatherings and restricts international travelling to maintain the decreasing trend in the infections trend. Yezli and Khan [ 36 ] argued that, besides the socio-economic, political, and religious challenges faced by the Kingdom of Saudi Arabia, the country took bold steps to restrain coronavirus through social distancing and complete lockdown. The country suddenly closed due to the high epidemic curve because of its social and religious norms hosting massive religious gatherings. These measures are essential in containing the virus, although at the cost of a severe economic crisis. The services industry mainly suffers due to restrictions being imposed on the travel and tourism sector, businesses shut down, closure of educational and other institutions, and maintaining social distancing; all of these measures would help to restrain the country’s epidemic curve. 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Note: SVAD shows services value-added, COMD shows communicable disease, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activity, EACT shows economic activity, PCONT shows price control, and SOCIDIS shows social distancing. LOG shows natural logarithm. C shows constant. Red lines shows the critical region.Blue line shows the estimated value. 4. Discussion Table 6 shows the endogeneity test results that were performed through quantile median regression. Financial development generally works as a growth proxy; thus, evaluating the possible endogeneity in the given model, the study performs the three-step procedure. The first step is to use ln (FACT) as a dependent variable that is replaced by ln (SVAD), while the remaining variables are exogenous variables and obtained its residual value (i.e. res_01). In the second step, ln (SVAD) is used again as a primary endogenous variable, while res_01 and other variables, except for ln (FACT), are used as regressors and obtain coefficient estimates. In the final step, the Wald coefficient restrictions are applied on the given res_01 term and found the statistically insignificant results of t-statistics, F-statistics, and Chi-square statistics. The results confirmed that there are no possible endogeneity issues in the quantile regression estimates. Thus, the results are valid and reliable. Table 6. Endogeneity Test performed by Quantiles Median Regression. Variables First Step: ln (SVAD) Second Step: ln (FACT) Final Step: ln (SVAD) Coefficient Std. Error t-Statistic Prob. Coefficient Std. Error t-Statistic Prob. Coefficient Std. Error t-Statistic Prob. C−2.875 1.550 −1.854 0.071 6.848 4.894 1.399 0.169 −3.374 1.741 −1.937 0.064 ln(FACT) −0.072 0.077 −0.946 0.350 N/A N/A N/A N/A N/A N/A N/A N/A ln(COMD) −0.115 0.041 −2.789 0.008 0.434 0.175 2.469 0.018 −0.146 0.030 −4.818 0.000 ln(WOM) 0.327 0.628 0.520 0.605 −1.080 1.638 −0.659 0.513 0.406 0.679 0.598 0.553 ln(lockdown) 0.247 0.092 2.674 0.011 −0.355 0.218 −1.627 0.112 0.273 0.072 3.764 0.000 ln(EACT) 0.418 0.186 2.240 0.031 −0.005 0.538 −0.011 0.991 0.419 0.187 2.240 0.031 ln(PCONT) −0.030 0.011 −2.717 0.009 −0.078 0.032 −2.427 0.020 −0.024 0.009 −2.477 0.017 ln(SOCDIS) −0.447 0.410 −1.090 0.287 1.880 1.118 1.681 0.100 −0.584 0.480 −1.217 0.231 Res_01 N/A N/A N/A N/A N/A N/A N/A N/A −0.072 0.077 −0.946 0.350 Adjusted R20.852 0.779 0.852 S.E. of regression 0.018 0.044 0.018 Quantile dependent variable 4.080 4.537 4.080 Sparsity 0.023 0.119 0.023 Wald Coefficient Restrictions Wald Test t-statistic: −0.946, p> 0.090 F-statistics: 0.896, p> 0.090 Chi-square statistic: 0.895, p> 0.090 Note: SVAD shows services value-added, COMD shows communicable disease, WOM shows word-of-mouth, LOCKDOWN shows lockdown, FACT shows financial activity, EACT shows economic activity, PCONT shows price control, res_01 shows residual term, and SOCIDIS shows social distancing. N/A shows not applicable. Table 7 shows the IRF estimates and suggested that smart lockdown services will positively influence services value-added, sound financial and economic activities, price control, and social distancing. In contrast, word-of-mouth and communicable diseases largely in160 [Document text truncated for crawler view.]