The monetary value of human life losses associated with COVID-19 in Africa: A human capital approach

Kirigia, Joses M.; Mwabu, Germano M. Article The monetary value of human life losses associated with COVID-19 in Africa: A human capital approach Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Kirigia, Joses M.; Mwabu, Germano M. (2025) : The monetary value of human life losses associated with COVID-19 in Africa: A human capital approach, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 8, pp. 1-48, https://doi.org/10.3390/economies13080241 This Version is available at: https://hdl.handle.net/10419/329521 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. 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/4.0/ Academic Editor: Gaurav Datt Received: 27 May 2025 Revised: 5 July 2025 Accepted: 9 July 2025 Published: 16 August 2025 Citation: Kirigia, J. M., & Mwabu, G. (2025). The Monetary Value of Human Life Losses Associated with COVID-19 in Africa: A Human Capital Approach. Economies,13(8), 241. https://doi.org/ 10.3390/economies13080241 Copyright: © 2025 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/). Article The Monetary Value of Human Life Losses Associated with COVID-19 in Africa: A Human Capital Approach Joses Muthuri Kirigia 1,* and Germano Mwabu 2 1African Sustainable Development Research Consortium (ASDRC), Nairobi P.O. Box 6994-00100, Kenya 2Department of Economics and Development Studies, University of Nairobi, Nairobi P.O. Box 30197-00100, Kenya; [email protected] *Correspondence: [email protected] Abstract Background: By 30 June 2021, the 54 African sovereign nations had reported 5,465,790 laboratory-confirmed COVID-19 cases (including 142,171 deaths). This study aimed to estimate the monetary value of human life losses, indirect and direct costs, and the potential cost reductions due to vaccinations for advocacy use by Ministries of Health in Africa. Methods: We employed both the human capital approach to value human lives lost and an abridged total cost-of-illness methodology to estimate the indirect and direct costs of COVID-19 across 54 African countries. The secondary data analyzed was from different sources. Results: The 142,171 human lives lost had an estimated discounted total monetary value of Int$6,684,101,196, i.e., Int$47,015 per life loss and Int$4.88 per person in the population. The estimated total cost of the actual reported 5,514,709 COVID-19 cases was Int$7,155,473,174, which comprised a total direct cost of Int$3,981,927,049 (55.6%) and an indirect cost of Int$3,173,546,125 (44.4%). We projected that vaccination of all the eligible people in the population would potentially save the African continent approximately Int$41,624,735,824. The average total saving per person is approximately Int$30.4. Conclusions: The COVID-19 pandemic resulted in substantial monetary value of human life losses and indirect and direct costs. Keywords: COVID-19; gross domestic product; value of life; indirect cost; direct cost; human capital approach; Africa JEL Classification: H51; I10; I19 1. Background The African continent, consisting of 54 sovereign countries and four territories (Mayotte, Réunion, Saint Helena, Western Sahara), has an estimated total population of 1.37 billion people (Worldometer,2021) and a total gross domestic product (GDP) of International Dollars (Int$) 6.86 trillion in 2021 (International Monetary Fund (IMF),2021). The African Union (AU) projects an economic growth of − 0.8% (for 2021) due to Coronavirus Disease (COVID-19), compared to the initial projection of +3.4% for 2020 (African Union (AU),2020). As of 30 June 2021, the African continent (comprising 54 sovereign states and four territories) had 5,514,709 confirmed COVID-19 cases, comprising 4,824,876 recovered cases, 547,261 active cases, and 142,572 deaths (Worldometer,2021). Of the latter, 142,171 deaths were reported by the 54 African sovereign nations, and the remaining 401 deaths (0.28%) by the territories of Saint Helena (0), Mayotte (174), Réunion (226), and Western Sahara Economies 2025,13, 241 https://doi.org/10.3390/economies13080241 Economies 2025,13, 241 2 of 48 (Worldometer,2021). The study focuses on the 5,465,790 cases reported by the sovereign states and does not include the 48,919 cases reported by the four territories. To mitigate the adverse socio-economic effects of COVID-19, the AU recommended that African governments should “boost investments that strengthen health systems to enable faster treatment [of infections] and containment” of the pandemic (p. 32) (African Union (AU),2020). However, there is evidence that the performance of health-related systems in Africa, both before and during the pandemic, has been suboptimal. For instance, concerning (a) national health systems (NHSs), the Universal Health Service Coverage Index was below 50% in approximately 70% of countries (36 out of 54) (WHO,2021a); (b) in the social determinants of health (SDH) systems, over 50% of the total population in 37 countries did not have basic sanitation services, drinking-water, and handwashing facilities at home (WHO,2021b); (c) in the national health research systems (NHRSs), the average World Health Organization (WHO) African region NHRS barometer score on the capacity to produce and utilize research findings was below 60% in 2018 (Rusakaniko et al., 2019). The underperformance of especially NHSs has been attributed to underinvestment (Asante et al.,2020) and economic inefficiencies (Nabyonga-Orem et al.,2023). Therefore, there is a need to generate evidence for advocacy to increase investments and improve the efficiency of allocating and utilizing health development resources, continually enhancing the performance of health-related systems in Africa, especially during pandemics. According to Rice (2000) and the World Health Organization (WHO,2009,2001), estimates of the money value of human life expressed in terms of foregone lifetime earnings are needed by health development policymakers for use in raising public health awareness and advocating with governments, private sector, and other stakeholders for increased and sustained investments in health-related systems for attainment of United Nations Sustainable Development Goal 3 (SDG3) on ensuring healthy lives and promoting wellbeing for all people (including those at risk of COVID-19 infection) at all ages (United Nations (UN),2015). Since the ministers of finance and the private sector chief executive officers who control resources for addressing health determinants are not usually public health experts, they may not fully appreciate the adverse effects of non-fatal disability and premature mortality from COVID-19 on macroeconomic indicators, such as the GDP (WHO/AFRO,2006). Therefore, Ministries of Health could utilize evidence of the monetary value of human lives and productivity losses in their advocacy and collaboration with other government sectors and stakeholders to prevent or reduce deaths and non-fatal disabilities. Niewiadomski et al. (2025) conducted a systematic review of the evidence on productivity losses resulting from health problems associated with the COVID-19 pandemic, based on data from population-level studies. Out of 38 studies eligible for review, 79% used the Human Capital Approach. Of the 33 studies eligible for quantitative comparison, the authors found that the productivity losses ranged from 0 to 2.1% of gross domestic product. Hanly et al. (2022) applied the human capital approach to estimate premature mortality productivity costs (indirect costs) associated with COVID-19 across nine countries (Belgium, France, Germany, Italy, the Netherlands, Portugal, Spain, Sweden and Switzerland) in Europe. Similar studies are relatively limited on the African continent. Thus, a rapid study was necessary to utilize the most recent secondary data to address three research questions. (a) What is the discounted monetary value of the cumulative number of human lives lost due to COVID-19 in Africa from the beginning of the pandemic till 30 June 2021? (b) What is the approximate magnitude of the direct health systems costs incurred in preventing and managing COVID-19 infections in Africa? (c) What is the approximate cost saving anticipated from COVID-19 vaccinations? The specific study objectives were the following: (a) To estimate the discounted monetary value of the cumulative number of human life losses in Africa associated with Economies 2025,13, 241 3 of 48 COVID-19 as of 30 June 2021. (b) To estimate the indirect and direct costs associated with the actual reported COVID-19 infections in Africa. (c) To project potential savings or reductions in indirect and direct health system costs, assuming 100% of the target population in Africa is vaccinated against COVID-19. 2. Methods This section outlines the methods employed to achieve the study objectives stated above. 2.1. Study Area and Population As already stated, the analysis reported in this paper is on the 54 sovereign states of the African continent (See Supplementary Table S1). Therefore, the analysis focuses on Africa’s sovereign states, with 5,465,790 laboratory-confirmed COVID-19 cases (including 142,171 deaths) as of 30 June 2021 (Worldometer,2021). Supplementary Table S2 summarizes the 4,794,317 recovered cases, 529,302 active cases, and 142,171 deaths from COVID-19 reported among sovereign states as of 30 June 2021 (Worldometer,2021). 2.2. Study Design We utilized a cross-sectional study design to collate administrative data on variables used in the analysis from secondary sources. 2.3. Conceptual Framework for Valuation of Human Life 2.3.1. Overview As Drummond et al. (2015) explain, three approaches exist to assign monetary values to health outcomes, such as human life. First, the willingness to pay (WTP) (or contingent valuation) approach uses survey methods to reveal the maximum amount households (or individuals) would hypothetically be willing to pay for a program (or intervention) that could potentially reduce the probability of a loss of a statistical life (i.e., the life of an unknown person) (Mooney,1977). Some strengths of the WTP approach, as highlighted by Mooney (1977) and Jones-Lee (1982,1985), include its ability to value non-economic aspects of the intervention (e.g., the intrinsic pleasure of being alive or disease-free) and its incorporation of consumer preferences. On the other hand, the approach’s main weaknesses are as follows: healthcare consumers (patients) may not be rational and sovereign decisionmakers, assume that individual preferences are static, WTP is a function of income and wealth only, and may be prone to response bias (Jones-Lee,1982;Mooney,1977). Moreover, applying the WTP approach was impossible due to the ongoing community circulation of various COVID-19 variants and the challenges of obtaining telephone numbers to administer WTP questionnaires remotely. The second approach is the implied values (or revealed preferences) method (IVA). The IVA is based on the values placed on preventing loss of life by past individuals or political decision-makers (Mooney,1977). One strength of IVA is that it relies on actual, implied life values in various health-related policy contexts. A politically designated person (e.g., a Member of Parliament) provides the values, and the information is readily available in public sector documents (Mooney,1977). However, the weaknesses of IVA include the following: decisions to invest in specific health projects are not determined by popular vote and might thus suffer from selection bias; similar lives may be valued differently depending on the public sector with which the person providing values is affiliated; the requirement values provided need to be consistent across the statistical lives being valued and cannot be met by such implicit and arbitrary valuations based on political processes and their outcomes (Mooney,1977;Jones-Lee,1982). Economies 2025,13, 241 4 of 48 The third method is the human capital approach (HCA), which was first applied by Petty (1699) in the 17th Century and whose theoretical underpinning was developed much later by Fein (1958), Mushkin and Collings (1959), Weisbrod (1971), and Landefeld and Seskin (1982). HCA values life in terms of the present value of future output lost (as proxied by potential earnings lost) due to premature mortality from any cause, e.g., malaria (Chima et al.,2003), non-communicable diseases (Kankeu et al.,2013), and COVID-19 (Musango et al.,2024). The method’s strengths include ease of understanding by policymakers, the use of objective data on earnings, and the routine collection of data on morbidity and mortality (Mooney,1977). However, according to Mooney (1977), the weaknesses of HCA include its focus on the livelihoods that people obtain from good health rather than the benefits of health per se, disregard for current preferences of potential beneficiaries, attachment of greater weight to the lives of the wealthy, and discrimination against those not in the labor market, e.g., homemakers, the retired, the unemployed, the severely handicapped, and children below the minimum work-age limit. In the study reported in this paper, the HCA was applied due to its strengths, the unfeasibility of collecting primary data due to the global COVID-19 pandemic at the time, and the availability of secondary data on GDP per capita, current health expenditure per capita, average life expectancy, and the number of COVID-19 deaths for all countries in Africa. 2.3.2. An HCA Model for Estimating the Discounted Monetary Value of Human Life Losses Associated with COVID-19 In line with the WHO guidelines on the measurement of the economic impact of disease and injuries (WHO,2009) and recent empirical COVID-19 studies (Musango et al., 2024), the current study estimated the monetary value of discounted aggregate flows of the current and future consumption of non-health goods and services foregone due to premature mortality (at 16 age groups) attributed to COVID-19. The current study used non-health GDP per capita in the estimations of the value of each statistical human life lost to COVID-19 in Africa. The non-health GDP per capita is the difference between GDP per capita and current health expenditure per capita for each sovereign state. According to Grossman (2000), an individual derives utility (happiness) from health, rather than from the consumption of healthcare. Thus, the demand for healthcare and other health inputs is derived from the basic demand for health. The African continent’s total monetary value (TMV Africa ) of human life losses associated with COVID-19 is the sum of each of the 54 countries’ total monetary value (TMV j=1,...,54 ) (Kankeu et al.,2013;Weisbrod,1971). Formulaically, the value is the following: TMVAfrica =∑i=54 i=1TMVj=1,...,54 (1) Further, each of the jth country’s TMV of human life losses from COVID-19 was estimated using the following formula (Kankeu et al.,2013;Musango et al.,2024): TMVj=∑i=16 i=1MVi=1,...,16 (2) where, ∑i=16 i=1(.) is the sum of discounted monetary values of human losses from COVID-19 in age groups 1 = 0–4 years, 2 = 5–9 years, 3 = 10–14, 4 = 15–19 years, 5 = 20–24 years, 6 = 25–29 years , 7 = 30–34 years, 8 = 35–39 years, 9 = 40–44 years, 10 = 45–49 years, 11 = 50–54 years , 12 = 55–59 years , 13 = 60–64 years, 14 = 65–69 years, 15 = 70–74 years, and 16 = 75 years and older; and MV i is the monetary value of a life lost for the ith age group. Only South Africa and Tunisia reported COVID-19 deaths in the 16 age groups. The breakdown of deaths associated with COVID-19 by the 16 age groups was unavailable for 52 countries. Since the Economies 2025,13, 241 5 of 48 COVID-19 deaths age breakdown was not available for other African countries, the age distribution for the two countries was used to break down the deaths sustained in the remaining 52 countries. The discounted MV i per age group was estimated using the following equation (Kankeu et al.,2013;Musango et al.,2024): MVi=∑t=n t=1n1/(1+r)t×GDPPCj−CEHPCj×ALEj−AADi×TCOVDj×Pio(3) where ∑t=n t=1(.) is the summation from the first year of life lost (t = 1) to the last year of life lost (t = n) per death in an age group; r is the discount rate, i.e., 3% in the current study; GDPPC j is the GDP per capita for the jth country in 2021; CEHPC j is the current expenditure on health per capita in the jth country in 2021; ALE j is the national average life expectancy in country j; AAD i is the average age at the onset of death in ith age group; TCOVD j is the total number of human lives lost from COVID-19 in the jth country as of 30 June 2021; and P i is the proportion of COVID-19 deaths borne by age group i. The base year for the calculations was 2021. 2.3.3. Data and Data Sources The monetary value of human life losses associated with COVID-19 in 54 African countries was calculated using eight data types. First, the discount rates of 3%, 5%, and 10% are commonly used in health-related studies, such as those by Musango et al. (2024), Haacker et al. (2020), Attema et al. (2018), and Edejer et al. (2003). Second, the data on the cumulative number of human lives lost from COVID-19 per country in Africa as of June 30, 2021, from the Worldometer Coronavirus Disease (COVID-19) Pandemic Database (Worldometer,2021) (See Supplementary Table S2). Third, the per capita GDP (GDPPC) in 2021 International Dollars or purchasing power parity (PPP) per country from the IMF World Economic Outlook Database (See Supplementary Table S3) (International Monetary Fund (IMF),2021). Fourth, the current expenditure on health per capita in 2021 International Dollars was projected using information from the WHO Global Health Expenditure Database (See Supplementary Table S4) (WHO,2019a). Fifth, the distribution of COVID-19-associated deaths across 16 age groups was unavailable for all 54 countries. It was only available for South Africa (Statista,2021a) and Tunisia (Statista,2021b). As explained in Section 2.3.2, the age distribution for the two countries was used to break down COVID-19 deaths in other African countries. Therefore, using the age structure of COVID-19 deaths in South Africa and Tunisia to break down deaths sustained in the remaining 52 African countries assumes that the distribution in these two countries reflects their general population distribution. Table 1, column 2 presents South Africa’s age distribution of COVID-19 deaths (Statista, 2021a). The 16 age-group proportions for South Africa were applied to Angola, Botswana, Burundi, Comoros, Democratic Republic of the Congo, Eswatini, Kenya, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Rwanda, Seychelles, South Africa, South Sudan, Tanzania, Uganda, Zambia, Zimbabwe, i.e., the East African Community [EAC] and Southern African Development Community [SADC] countries. On the other hand, the COVID-19 deaths age group distribution for Tunisia is displayed in Table 1, column 3 (Statista,2021b). These proportions were applied to member countries of the Arab Maghreb Union [AMU] (Algeria, Libya, Mauritania, Morocco, Tunisia), the Central African Economic and Monetary Community [CEMAC] (Cameroon, Chad, the Central African Republic, Equatorial Guinea, Gabon, the Republic of Congo, Sao Tome and Principe), and the Economic Community of West African States [ECOWAS] (Benin, Burkina Faso, Cabo Verde, Cote d’Ivoire, The Gambia, Ghana, Guinea, GuineaBissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Togo). Although Angola and Economies 2025,13, 241 6 of 48 the DRC are members of both CEMAC and SADC, Burundi and Rwanda are members of both EAC and CEMAC. The age distribution for South Africa was applied to these four countries. Table 1. South Africa and Tunisia’s age distribution of COVID-19 deaths. Age Group (Years) South Africa (Percent) * Tunisia (Percent) ** 0–4 years 0.275 0.014 5–9 years 0.175 0.000 10–14 years 0.175 0.014 15–19 years 0.375 0.078 20–24 years 0.575 0.071 25–29 years 1.075 0.107 30–34 years 2.075 0.170 35–39 years 3.275 0.448 40–44 years 4.575 0.618 45–49 years 6.575 1.051 50–54 years 8.975 2.223 55–59 years 12.675 4.482 60–64 years 14.175 9.192 65–69 years 13.175 18.802 70–74 years 10.675 23.114 75 years and older 21.175 39.615 Source: * Statista (2021a); ** Statista (2021b). Sixth, the average life expectancy (ALE) for each country was extracted from the Worldometer Database (See Supplementary Table S5) (Worldometer,2021). The world’s highest average life expectancy at birth, 88 years for females in Hong Kong, was used in the sensitivity analysis from the same database. 2.3.4. Data Analysis Equations (1)–(3) were estimated using Microsoft Excel software (Microsoft Corporation, New York, NY, USA), and the analysis involved 12 steps. Step 1: Equations (2) and (3), presented in Section 2.3.2, were constructed into 54 Excel sheets (i.e., one sheet per country) to calculate the MVi per ith age group. The summation operation derives the jth country’s TMV of human life losses from COVID-19. Step 2: Equation (1) was incorporated into a separate Excel sheet to derive the TMVAfrica of human life losses associated with COVID-19, i.e., to calculate the total monetary value for each of the 54 countries. Step 3: In each country, the number of COVID-19 deaths per age group is equal to the total number of deaths from COVID-19 multiplied by the respective age group proportion (see Supplementary Table S6). For example, Algeria had lost 3708 lives to COVID-19 by 30 June 2021, and the proportion of these deaths in the 45–49-year age group was 0.0105128569399062 (1.051%). Thus, the number of deaths borne by the 45–49-year-olds equals 39, i.e., 3708 times 0.0105128569399062. Step 4: Since the latest current expenditure on health per capita (CEHPC) available at the time in the WHO Global Health Expenditure Database was for 2018 (at the time of the analysis), it was necessary to forecast each country’s CEHPC for 2021 us- Economies 2025,13, 241 7 of 48 ing existing 2017 and 2018 data (see Supplementary Table S4). The CEHPC growth rate between the years 2017 (CEHPC2017) and 2018 (CEHPC2018) equals [(CEHPC2018 − CEHPC2017)/CEHPC2017]. For example, Algeria’s CEHPC in 2017 was Int$970.26824951, and its CEHPC in 2018 was Int$962.71936035. The growth rate between 2017 and 2018 equals − 0.00778020837414019, i.e., [(962.71936035 − 970.26824951)/970.26824951]. Thus, the projections across the years 2019 to 2021 assume that the growth rate between 2017 and 2018 will be sustained, i.e., it will remain constant. Step 5: The non-health GDP per capita (NHGDPPC j ) for each of the 54 countries was calculated by subtracting the respective CEHPC from GDPPC (see Supplementary Table S4). For instance, since Algeria’s GDP per capita was Int$11,435 and CEHPC was Int$940 in 2021, the non-health GDP per capita equals Int$10,495, i.e., 11,435 minus 940. Step 6: The average age at onset of death (AAD i ) for each of the first 15 age groups in Table 2was calculated as a simple average, e.g., the AAD for 0–4-year-olds equals 2 years, which is (0 + 4)/2. We assumed an AAD of 75 years for those 75 years and older. There was no individual country-specific data on the number of deaths at each age that could have shown variations across countries. Therefore, the mid/average age for each of the 16 age groups was used to construct the average age at death. Table 2. Average age of onset of COVID-19 death per age group. Age Group Average Age of Onset (Years) * 0–4 years 2 5–9 years 7 10–14 years 12 15–19 years 17 20–24 years 22 25–29 years 27 30–34 years 32 35–39 years 37 40–44 years 42 45–49 years 47 50–54 years 52 55–59 years 57 60–64 years 62 65–69 years 67 70–74 years 72 Source: * Author’s estimates. Step 7: The undiscounted years of life lost (UDYLL) for each age group was estimated as the difference between the respective country’s ALE and the group’s AAD i . For example, given that the ALE for Algeria was 77.5 years and the AAD for the 45–49 age group was 47 years, UDYLL = 77.5 −47 = 30.5 years. Step 8: The UDYLLs estimated in Step 7 were discounted (at a rate of 3%) because people prefer health (or monetary) benefits today rather than in the future. Drummond et al. (2015) explained that people might have a positive rate of time preference because they have a short-term view of life, uncertainties regarding the future, and positive economic growth. The discounted year of life (DYLL) was calculated by multiplying each UDYLL by the corresponding discount factor. For example, the discount factor for Economies 2025,13, 241 8 of 48 the first YLL = 1/(1 + r)t= 1/(1 + 0.03)1= 0.970874 . The discount factor for the thirty-first YLL = 1/(1 + r)t= 1/(1 + 0.03)31 = 0.399987 . The summation of discount factors from the first YLL to the thirty-first YLL (see Step 7) in Algeria yields 20 discounted YLL (DYLL). Step 9: The discounted monetary value (MV i ) per ith age group is the product of DYLL, NHGDPPC j , and the number of COVID-19 deaths per age group (TCOVDj). The calculation can be illustrated using the age group 44–49 years in Algeria, where DYLL44–49 = 20 years (from Step 8), NHGDPPC = $10,495 (see Step 5), and TCOVD44-49 = 39 deaths (see Step 3). The MV for 44–49-year-olds = DYLL 44–49 × NHGPP × TCOVD 44–49 = 20 × 10,495 × 39 = Int$8,186,100. The monetary values for the remaining 15 age groups were calculated similarly, applying Equation (3). Step 10: Applied Equation (2) to sum up the monetary values of human lives lost in the 16 age groups to yield the TMV for each country. Step 11: Equation (3) was used to sum up the TMVs across the 54 countries to derive the total continental loss. Step 12: Sensitivity analysis. In the baseline model used to estimate each country’s TMV, we assumed (a) a 3% discount rate and (b) each country’s ALE. There is no consensus in the published literature regarding the two variables; thus, uncertainty exists. In such a situation, it is standard practice in epidemiology (Thabane et al.,2013) and health economics (Drummond et al.,2015) to rerun the model with different variable values to assess the robustness and credibility of the result(s), e.g., the TMV in our case. Therefore, as conducted in past health economics studies in Africa and elsewhere, we re-estimated the economic model using 3%, 5%, and 10% discount rates, while holding other variables, such as the number of COVID-19 deaths per country, per capita GDP, and CEHPC, constant. Furthermore, in line with past practice, the economic model was rerun three times using each country’s ALE (See Supplementary Table S5); the highest ALE in Africa was 77.5 years, and the world’s highest ALE was 88 years among females in Hong Kong (Worldometer,2021). 2.4. Conceptual Framework for Estimating the Cost of COVID-19 in Africa The total economic cost of COVID-19 (TC COVID-19 ) encompasses total indirect costs ( TICCOVID-19 ), total direct costs ( TDCCOVID-19 ), and psychic/intangible costs ( TPCCOVID-19 ), i.e., TCCOVID-19 =TICCOVID-19 +TDCCOVID-19 +TPCCOVID-19 (4) 2.4.1. Total Indirect Cost Algorithm TICCOVID-19 =VPYLLCOVID-19 +VPTLNFL +VFTL (5) where VPYLLCOVID-19 is the value of potentially productive YLL due to premature death from COVID-19 among those within the working-age bracket (i.e., 15–64 years) in country j; VPTLNFL is the value of productive time lost among non-fatal COVID-19 cases in a specific working-age bracket; VFTL is the value of work time lost among all family members (and friends) of working-age accompanying and/or visiting patients. VPYLLCOVID-19 =TMV15−64 ×LFPR (6) where TMV15−64 is the jth country’s total monetary value of YLL in 15–64 age bracket (see Section 2.3.2); LFPR is the proportion of jth country’s working-age population that actively engages in the labor market, either by working or looking for work (see Supplementary Table S7) (World Bank,2021). In principle, the workforce participation rates in the ten age groups within the 15–64-year productive bracket would be expected to vary. In addition, there is evidence that those aged 50 years and above are more incapacitated by COVID-19 infections than those in younger age groups (15–49 years). However, due to the unavail- Economies 2025,13, 241 15 of 48 Table 5. The discounted monetary value of actual human life losses from COVID-19 in Africa as of 30 June 2021 (in 2021 Int$ or PPP). Country Population in 2021 * (A) COVID-19 Deaths as of 30 June 2021 * (B) Total Discounted Monetary Value of Human Lives Lost (Int$) [C] ** Discounted Monetary Value per Human Life Lost (Int$) [D = C/B] ** The Discounted Monetary Value of Human Life Lost per Person in the Population (Int$) [E = C/A] ** Algeria 44,634,463 3708 269,465,205 72,671 6.04 Angola 33,874,015 894 28,607,378 31,999 0.84 Benin 12,436,641 104 345,211 3319 0.03 Botswana 2,398,576 1125 143,566,755 127,615 59.85 Burkina Faso 21,468,861 168 363,206 2162 0.02 Burundi 12,239,994 8 27,418 3427 0.00 Cameroon 27,198,364 1324 3,157,829 2385 0.12 Cape Verde 561,973 286 7,888,336 27,582 14.04 Central African Republic 4,912,863 98 14,300 146 0.00 Chad 16,890,785 174 100,388 577 0.01 Comoros 887,911 146 2,403,529 16,463 2.71 Congo, Republic of 5,652,216 165 849,559 5149 0.15 Cote d’Ivoire 27,023,309 313 990,128 3163 0.04 Congo, Democratic Republic of 92,245,852 924 4,024,545 4356 0.04 Equatorial Guinea 1,448,396 121 1,367,596 11,302 0.94 Eritrea 3,595,038 23 70,886 3082 0.02 Ethiopia 117,775,639 4320 21,655,959 5013 0.18 Gabon 2,277,613 159 3,850,813 24,219 1.69 Gambia, The 2,483,649 181 390,186 2156 0.16 Ghana 31,714,153 795 5,678,026 7142 0.18 Guinea 13,484,325 169 405,703 2401 0.03 Guinea-Bissau 2,013,948 69 96,821 1403 0.05 Kenya 54,941,831 3621 113,571,447 31,365 2.07 Lesotho 2,159,095 329 2,383,283 7244 1.10 Liberia 5,175,111 127 240,645 1895 0.05 Madagascar 28,393,805 911 10,125,826 11,115 0.36 Malawi 19,617,945 1194 6,397,062 5358 0.33 Mali 20,826,158 525 953,852 1817 0.05 Mauritania 4,770,294 487 3,849,297 7904 0.81 Economies 2025,13, 241 16 of 48 Table 5. Cont. Country Population in 2021 * (A) COVID-19 Deaths as of 30 June 2021 * (B) Total Discounted Monetary Value of Human Lives Lost (Int$) [C] ** Discounted Monetary Value per Human Life Lost (Int$) [D = C/B] ** The Discounted Monetary Value of Human Life Lost per Person in the Population (Int$) [E = C/A] ** Mauritius 1,273,865 18 4,169,354 231,631 3.27 Mozambique 32,119,351 872 4,592,429 5267 0.14 Namibia 2,586,431 1445 72,902,503 50,452 28.19 Niger 25,069,087 193 262,578 1361 0.01 Nigeria 211,184,869 2120 3,996,746 1885 0.02 Rwanda 13,269,271 431 7,523,452 17,456 0.57 São Tomé and Príncipe 223,185 37 378,869 10,240 1.70 Senegal 17,179,451 1166 8,699,508 7461 0.51 Seychelles 98,951 68 16,916,076 248,766 170.95 Sierra Leone 8,137,375 98 56,817 580 0.01 South Africa 60,049,601 60,264 3,739,829,800 62,057 62.28 South Sudan 11,323,788 117 335,752 2870 0.03 Swaziland 1,172,073 678 23,916,056 35,274 20.40 Tanzania 61,412,589 21 351,667 16,746 0.01 Togo 8,470,400 129 157,661 1222 0.02 Uganda 47,164,701 989 13,032,941 13,178 0.28 Zambia 18,891,903 2138 35,519,279 16,613 1.88 Zimbabwe 15,077,192 1761 20,034,980 11,377 1.33 Djibouti 1,002,228 155 1,504,127 9704 1.50 Egypt 104,243,582 16,148 714,752,930 44,263 6.86 Libya 6,963,848 3191 94,931,967 29,750 13.63 Morocco 37,344,128 9292 428,373,900 46,101 11.47 Somalia 16,330,692 775 347,169 448 0.02 Sudan 44,860,676 2754 13,457,222 4886 0.30 Tunisia 11,941,219 14,843 845,216,224 56,944 70.78 TOTAL 1,370,493,279 142,171 6,684,101,196 47,015 4.88 Sources: * Worldometer (2021); ** Authors’ estimates. As depicted in Figure 1, the TMV varied widely from a minimum of Int$14,300 in the Central African Republic to a maximum of Int$3,739,829,800 in South Africa. Economies 2025,13, 241 17 of 48 269,465,205 28,607,378 345,211 143,566,755 363,206 27,418 3,157,829 7,888,336 14,300 100,388 2,403,529 849,559 990,128 4,024,545 1,367,596 70,886 21,655,959 3,850,813 390,186 5,678,026 405,703 96,821 113,571,447 2,383,283 240,645 10,125,826 6,397,062 953,852 3,849,297 4,169,354 4,592,429 72,902,503 262,578 3,996,746 7,523,452 378,869 8,699,508 16,916,076 56,817 3,739,829,800 335,752 23,916,056 351,667 157,661 13,032,941 35,519,279 20,034,980 1,504,127 714,752,930 94,931,967 428,373,900 347,169 13,457,222 845,216,224 - 1,000,000,000 2,000,000,000 3,000,000,000 4,000,000,000 Algeria Benin Burkina Faso Cameroon Central African Republic Comoros Cote D'Ivoire Equatorial Guinea Ethiopia Gambia Guinea Kenya Liberia Malawi Mauritania Mozambique Niger Rwanda Senegal Sierra Leone South Sudan Tanzania Uganda Zimbabwe Egypt Morocco Sudan International Dollars (Int$) Figure 1. Discounted total monetary value of human life losses associated with COVID-19 in Africa by 30 June 2021 (in International Dollars). Economies 2025,13, 241 18 of 48 Figure 2portrays the distribution of TMV across the 16 age groups. About Int$133,548,537 (2.0%) accrued to 0–14-year-olds; Int$4,591,809,977 (68.7%) to 15–59-yearolds; and Int$1,958,742,682 (29.3%) to 60-year-olds and above. 60,586,561 36,021,066 36,940,910 82,974,899 114,056,479 196,791,749 346,905,641 517,582,921 636,396,282 787,918,794 914,403,399 994,779,813 809,865,422 664,777,066 331,010,713 153,089,481 - 200,000,000 400,000,000 600,000,000 800,000,000 1,000,000,000 1,200,000,000 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75 and older International Dollars (Int$) Age group in years Figure 2. Discounted monetary value of human life losses from COVID-19 by age group in Africa (Int$). About Int$133,548,537 (2.0%) accrued to 0 − 14-year-olds; Int$4,591,809,977 (68.7%) to 15 − 59-year-olds; and Int$1,958,742,682 (29.3%) to 60-year-olds and above. Thus, most of the monetary value of lives lost to COVID-19 accrued to the most socioeconomically productive age bracket, i.e.,15–59-year-olds. The average monetary value was Int$47,015 per human life lost, and the monetary value of human life lost per person in the population was Int$4.88. The average TMV per COVID-19 death varied widely from a minimum of Int$146 in the Central African Republic to a maximum of Int$248,766 in Seychelles. The five countries with the highest average TMV per human life included Algeria with Int$72,671, Botswana with Int$127,615, Mauritius with Int$231,631, Seychelles with Int$248,766, and South Africa with Int$62,057. 3.2. Economic Cost of COVID-19 in Africa As explained earlier in Section 2.4 of the methods, the estimates of the total economic cost of COVID-19 (TC COVID-19 ) reported in this paper encompass only total indirect costs (TIC) and total direct costs (TDC). Economies 2025,13, 241 19 of 48 3.2.1. Indirect Cost of COVID-19 in Africa Table 6presents the total and average indirect costs (productivity losses) incurred per country as of 30 June 2021, due to COVID-19. Table 6. Total and average indirect cost among 15–64 years old by country in Africa (in 2021 Int$ or PPP). Country (A). COVID-19 Deaths 2021 * (B). Total Monetary Value of Lives Lost to COVID-19 (Int$) *** (C). Labor Force Participation Rate for Ages 15–64 (%) ** (D). To Indirect Cost (Int$) [D = Bx(C/100)] *** (E). Average Indirect Cost [E = (D/A)] *** Algeria 684 109,107,102 46.4 50,625,695 74,040 Angola 486 27,554,096 77.9 21,464,641 44,176 Benin 19 342,422 71.7 245,517 12,802 Botswana 611 133,347,604 73.0 97,343,751 159,205 Burkina Faso 31 360,381 67.8 244,338 7887 Burundi 4 26,455 80.0 21,164 4868 Cameroon 244 3,121,502 76.9 2,400,435 9832 Cape Verde 53 4,694,663 63.9 2,999,890 56,882 Central African Rep 18 13,977 72.3 10,106 559 Chad 32 98,344 70.7 69,529 2167 Comoros 79 2,329,866 46.6 1,085,718 13,682 Congo 30 844,274 70.3 593,524 19,507 Cote d’Ivoire 58 977,392 54.6 533,656 9246 DRC 502 3,871,323 64.1 2,481,518 4941 Equatorial Guinea 22 1,351,863 63.2 854,378 38,291 Eritrea 4 62,991 81.3 51,212 12,075 Ethiopia 797 19,419,954 81.3 15,788,423 19,819 Gabon 29 3,831,028 54.7 2,095,572 71,473 Gambia The 33 387,034 60.5 234,155 7016 Ghana 147 5,642,699 69.2 3,904,748 26,636 Guinea 31 402,426 63.0 253,528 8135 Guinea Bissau 13 95,658 72.9 69,735 5481 Kenya 1968 110,454,961 74.6 82,399,401 41,869 Lesotho 179 2,244,799 69.9 1,569,114 8775 Liberia 23 239,148 77.1 184,383 7873 Madagascar 495 9,675,527 87.2 8,437,060 17,040 Malawi 649 6,212,044 77.3 4,801,910 7400 Mali 97 944,317 71.3 673,298 6955 Mauritania 90 3,827,676 46.5 1,779,869 19,820 Mauritius 10 3,467,754 66.2 2,295,653 234,657 Mozambique 474 4,422,875 78.3 3,463,111 7307 Namibia 785 70,668,214 60.6 42,824,938 54,529 Niger 36 260,749 73.4 191,390 5378 Nigeria 391 3,922,315 56.7 2,223,953 5689 Economies 2025,13, 241 20 of 48 Table 6. Cont. Country (A). COVID-19 Deaths 2021 * (B). Total Monetary Value of Lives Lost to COVID-19 (Int$) *** (C). Labor Force Participation Rate for Ages 15–64 (%) ** (D). To Indirect Cost (Int$) [D = Bx(C/100)] *** (E). Average Indirect Cost [E = (D/A)] *** South Sudan 64 320,264 73.8 236,355 3717 Rwanda 234 6,987,929 84.1 5,876,849 25,088 Sao Tome et Principe 7 281,197 59.9 168,437 24,687 Senegal 215 7,213,397 47.1 3,397,510 15,802 Seychelles 37 14,790,765 66.6 9,849,170 266,496 Sierra Leone 18 55,759 58.8 32,786 1814 South Africa 32,753 3,625,212,884 60.1 2,178,752,943 66,520 Swaziland (Eswatini) 368 22,951,859 54.7 12,554,667 34,070 Tanzania 11 341,496 84.5 288,564 25,283 Togo 24 156,146 58.5 91,346 3840 Uganda 538 12,607,006 70.9 8,938,367 16,629 Zambia 1162 34,430,697 75.1 25,857,454 22,252 Zimbabwe 957 19,295,282 84.0 16,208,037 16,934 Djibouti 29 1,348,824 63.7 859,201 30,061 Egypt 2978 460,883,696 47.9 220,763,290 74,139 Libya 588 61,213,594 52.8 32,320,778 54,928 Morocco 1713 188,853,193 48.7 91,971,505 53,676 Somalia 143 342,073 49.4 168,984 1182 Sudan 508 13,381,636 49.7 6,650,673 13,096 Tunisia 2737 396,784,269 51.5 204,343,899 74,658 TOTAL (Int$) 54,210 5,401,675,398 3,173,546,125 58,542 Sources: * Worldometer (2021), ** International Monetary Fund (IMF) (2021), *** Authors’ estimates. Approximately 54,210 of the persons who died of COVID-19 in Africa were aged 15–64 years. Those lives had a monetary value of Int$5.402 billion. Adjustment for labor force participation rate yielded a Continental total indirect cost of Int$3,173,546,125 and an average of Int$58,542 per life lost in the 15–64 years bracket. Twenty-two countries (40.7%) had a total indirect cost of less than Int$1 million; 18 countries (33.3%) had an indirect cost of Int$1 million to Int$10 million; and 14 countries (26.0%) had an indirect cost of Int$11 million and above. Figure 3illustrates that the total indirect cost varied widely, ranging from Int$10,106 in the Central African Republic to a maximum of Int$2,178,752,943 in South Africa. Figure 4presents the average indirect cost of COVID-19 in Africa. The five countries with the highest average indirect cost per life lost were Seychelles, with Int$266,496; Mauritius, with Int$234,657; Botswana, with Int$159,205; Tunisia, with Int$74,658; and Egypt, with Int$74,139. The average indirect cost per COVID-19 death varied from a minimum of Int$559 in the Central African Republic to a maximum of Int$266,496 in Seychelles. One (1.9%) country had less than Int$1000 per life lost; 20 (37.0%) countries had between Int$1000 and Int$10,000; 11 (20.4%) countries had between Int$11,000 and Int$20,000; 6 (11.1%) countries had between Int$21,000 and Int$30,000; 16 (29.6%) countries had Int$31,000 and above. Economies 2025,13, 241 21 of 48 2,178,752,943 220,763,290 204,343,899 97,343,751 91,971,505 82,399,401 50,625,695 42,824,938 32,320,778 25,857,454 21,464,641 16,208,037 15,788,423 12,554,667 9,849,170 8,938,367 8,437,060 6,650,673 5,876,849 4,801,910 3,904,748 3,463,111 3,397,510 2,999,890 2,481,518 2,400,435 2,295,653 2,223,953 2,095,572 1,779,869 1,569,114 1,085,718 859,201 854,378 673,298 593,524 533,656 288,564 253,528 245,517 244,338 236,355 234,155 191,390 184,383 168,984 168,437 91,346 69,735 69,529 51,212 32,786 21,164 10,106 - 500,000,000 1,000,000,000 1,500,000,000 2,000,000,000 2,500,000,000 South Africa Egypt Tunisia Botswana Morocco Kenya Algeria Namibia Libya Zambia Angola Zimbabwe Ethiopia Swaziland (Eswatini) Seychelles Uganda Madagascar Sudan Rwanda Malawi Ghana Mozambique Senegal Cape Verde Is DRC Cameroon Mauritius Nigeria Gabon Mauritania Lesotho Comoros Djibouti Equatorial Guinea Mali Congo Cote d'Ivoire Tanzania Guinea Benin Burkina Faso South Sudan Gambia Niger Liberia Somalia Sao Tome et Principe Togo Guinea Bissau Chad Eritrea Sierra Leone Burundi Central African Rep International Dollars (or Purchasing Power Parity) Figure 3. Total indirect cost of COVID-19 by country in Africa (in 2021 Int$ or PPP). Economies 2025,13, 241 22 of 48 266,496 234,657 159,205 74,658 74,139 74,040 71,473 66,520 56,882 54,928 54,529 53,676 44,176 41,869 38,291 34,070 30,061 26,636 25,283 25,088 24,687 22,252 19,820 19,819 19,507 17,040 16,934 16,629 15,802 13,682 13,096 12,802 12,075 9,832 9,246 8,775 8,135 7,887 7,873 7,400 7,307 7,016 6,955 5,689 5,481 5,378 4,941 4,868 3,840 3,717 2,167 1,814 1,182 559 - 100,000 200,000 300,000 Seychelles Botswana Egypt Gabon Cape Verde Is Namibia Angola Equatorial Guinea Djibouti Tanzania Uni Rep Sao Tome et Principe Mauritania Congo Zimbabwe Senegal Sudan Eritrea Cote d'Ivoire Guinea Liberia Mozambique Mali Guinea Bissau DRC Togo Chad Somalia International Dollars (Int$) Figure 4. Average indirect cost of COVID-19 by country in Africa (in 2021 Int$ or PPP). 3.2.2. Direct Cost of COVID-19 in Africa As depicted in Table 7, the total direct health system cost of preventing and managing COVID-19 cases in Africa is estimated at Int$3,981,927,049. Five countries (Algeria, Egypt, Morocco, Tunisia, and South Africa) alone bore 86.7% (Int$3.45 billion) of the Continent’s total direct cost. Economies 2025,13, 241 23 of 48 Table 7. Estimated direct cost of preventing and managing COVID-19 cases as of 30 June 2021. Country (A) Total COVID-19 Cases * (B) Current Health Expenditure per Capita in 2021 (Int$) ** (C) Direct Cost (Int$) [C = A ×B)] *** Algeria 139,229 940 130,934,190 Angola 38,682 115 4,448,923 Benin 8199 85 700,219 Botswana 69,680 1082 75,380,140 Burkina Faso 13,479 73 977,779 Burundi 5428 89 485,399 Cabo Verde 32,457 481 15,617,354 Cameroon 80,858 154 12,492,030 Central African Republic 7141 540 3,855,622 Chad 4951 62 306,781 Comoros 3912 144 562,929 Congo 12,596 51 643,920 Djibouti 11,602 136 1,580,972 DRC 40,836 18 720,805 Egypt 281,031 491 138,124,544 Equatorial Guinea 8734 585 5,105,836 Eritrea 5936 139 823,562 Eswatini 19,084 659 12,581,054 Ethiopia 276,037 70 19,349,040 Gabon 24,984 482 12,034,455 Gambia 6079 76 464,312 Ghana 95,642 244 23,291,505 Guinea 23,753 138 3,272,720 Guinea-Bissau 3853 137 529,337 Cote d’Ivoire (Ivory Coast) 48,242 181 8,746,361 Kenya 183,603 263 48,349,428 Lesotho 11,344 399 4,529,139 Liberia 3900 59 230,223 Libya 193,238 8 1,559,041 Madagascar 42,207 60 2,518,105 Malawi 35,897 115 4,133,405 Mali 14,422 87 1,248,415 Mauritania 20,747 219 4,553,581 Mauritius 1833 1741 3,190,929 Morocco 530,585 560 297,383,335 Mozambique 75,828 143 10,815,752 Economies 2025,13, 241 24 of 48 Table 7. Cont. Country (A) Total COVID-19 Cases * (B) Current Health Expenditure per Capita in 2021 (Int$) ** (C) Direct Cost (Int$) [C = A ×B)] *** Namibia 86,649 778 67,442,498 Niger 5488 79 434,235 Nigeria 167,543 273 45,673,668 Rwanda 38,198 279 10,639,375 São Tomé and Príncipe 2366 215 507,569 Senegal 42,957 156 6,702,574 Seychelles 15,579 1922 29,950,243 Sierra Leone 5495 264 1,447,951 Somalia 14,933 30 447,990 South Africa 1,954,466 1256 2,454,780,059 South Sudan 10,834 52 561,673 Sudan 36,658 243 8,922,926 Tanzania 509 123 62,703 Togo 13,881 137 1,899,448 Tunisia 414,182 1036 429,152,254 Uganda 79,434 163 12,932,700 Zambia 152,056 324 49,332,595 Zimbabwe 48,533 196 9,495,444 TOTAL 5,465,790 3,981,927,049 Source: * Worldometer (2021), ** Authors’ projections using data from WHO (2019a), *** Authors’ estimates. Figure 5shows that the direct costs of preventing and managing COVID-19 infections vary widely from a minimum of Int$62,703 in Tanzania to a maximum of Int$2,454,780,059 in South Africa. Sixteen (30%) of the countries had a total direct cost of under Int$1 million, 18 (33%) countries had between Int$1 million and Int$10 million, and the remaining 20 (37%) countries had a total direct cost of Int$11 million and above. The main direct cost drivers are the size of per capita current health expenditure, and the number of COVID-19 cases identified through laboratory testing per country. The latter depends on the proportion of the population tested and the number of those who are reported positive. There is evidence that the population coverage of COVID-19 testing is very low in most African countries. Therefore, the direct cost estimates reported in this paper could be underestimated. Economies 2025,13, 241 31 of 48 Table 9. Cont. Country Control Group Direct Cost (Int$) Vaccine Group Direct Cost (Int$) Gabon 31,707,221 10,719,555 Gambia, The 5,482,538 1,853,533 Ghana 223,211,109 75,463,056 Guinea 53,694,939 18,153,147 Guinea-Bissau 7,996,421 2,703,424 Kenya 418,146,512 141,366,680 Lesotho 24,913,510 8,422,742 Liberia 8,829,117 2,984,942 Madagascar 48,958,347 16,551,804 Malawi 65,285,562 22,071,697 Mali 52,102,243 17,614,690 Mauritania 30,259,150 10,229,992 Mauritius 64,090,232 21,667,581 Mozambique 132,405,996 44,763,727 Namibia 58,181,480 19,669,954 Niger 57,327,529 19,381,251 Nigeria 1,663,857,712 562,515,851 Rwanda 106,816,010 36,112,282 Sao Tome and Principe 1,383,754 467,819 Senegal 77,469,502 26,190,835 Seychelles 5,497,873 1,858,717 Sierra Leone 61,970,418 20,950,916 South Africa 483,391,950 163,424,812 South Sudan 16,966,806 5,736,126 Swaziland 22,331,405 7,549,786 Tanzania 218,647,124 73,920,067 Togo 33,498,430 11,325,126 Uganda 221,928,779 75,029,526 Zambia 177,141,323 59,887,815 Zimbabwe 85,253,530 28,822,454 Djibouti 3,947,041 1,334,413 Egypt 1,480,743,156 500,608,610 Libya 1,623,783 548,967 Morocco 604,919,747 204,510,845 Somalia 14,159,230 4,786,942 Sudan 315,586,269 106,693,185 Tunisia 357,587,739 120,893,013 If the unit cost per COVID-19 case managed equals the respective country’s per capita current health expenditure, the continental total direct cost without vaccination is Int$9,459,720,500. Contrastingly, the total continental direct cost of the Oxford–AstraZeneca vaccination is estimated at Int$3,198,135,685. Economies 2025,13, 241 32 of 48 Figure 8shows that COVID-19 vaccination could potentially save Africa at least Int$6,261,584,816 in direct health systems costs. 1,101,341,861 980,134,546 802,996,067 400,408,902 319,967,138 276,779,832 236,694,727 208,893,084 157,930,982 147,748,054 146,899,253 144,727,057 117,253,509 93,726,056 87,642,270 80,384,542 74,530,213 70,703,728 56,431,076 51,278,667 50,744,593 49,638,885 43,213,865 42,422,651 41,019,502 38,511,526 37,946,278 35,541,792 34,487,553 32,406,543 31,148,801 29,792,819 22,173,304 20,987,666 20,939,206 20,318,695 20,029,157 20,021,916 16,490,768 16,197,975 14,781,619 11,230,680 9,541,700 9,372,287 5,844,176 5,527,613 5,292,997 5,172,902 3,639,156 3,629,006 2,612,628 2,444,240 1,074,816 915,936 - 400,000,000 800,000,000 1,200,000,000 Nigeria Egypt Algeria Morocco South Africa Kenya Tunisia Sudan Ethiopia Ghana Uganda Tanzania Zambia Cote d'Ivoire Mozambique Cameroon Angola Rwanda Zimbabwe Senegal Central African Republic Botswana Malawi Mauritius Sierra Leone Namibia Niger Guinea Mali Madagascar Congo, Democratic Republic of Burkina Faso Togo Gabon Burundi Benin Mauritania Chad Lesotho Equatorial Guinea Swaziland South Sudan Eritrea Somalia Liberia Congo, Republic of Guinea-Bissau Cape Verde Seychelles Gambia, The Djibouti Comoros Libya Sao Tome and Principe International Dollars (Int$) Figure 8. Total direct cost savings with COVID-19 Oxford–AstraZeneca vaccination in Africa (in 2021 Int$ or PPP). Economies 2025,13, 241 33 of 48 The direct cost savings due to the Oxford–AstraZeneca vaccine range from a minimum of Int$915,936 in São Tomé and Principe to a maximum of Int$1,101,341,861. About 12 (22.2%) countries are expected to make direct cost savings of less than Int$10 million; 21 (38.9%) countries between Int$10 million and Int$50 million; 8 (14.8%) countries between Int$51 million and Int$100 million; and 13 (24.1%) countries with savings of Int$101 million and above. 3.3.2. Savings in Potential Indirect Costs of COVID-19 in Africa Expected from COVID-19 Vaccination Applying the risk of death reported by Bernal et al. (2021), it is estimated that 1,656,615 unvaccinated persons aged 15–64 years would die from COVID-19 compared to 289,880 deaths among those vaccinated. We estimate that the total indirect cost in Africa is Int$42,863,554,186 among unvaccinated persons aged 15–64 years and Int$7,500,403,177 among those vaccinated. Thus, the total indirect cost saving from vaccination is approximately Int$35,363,151,009 in Africa. Figure 9shows that the total indirect cost savings from the COVID-19 Oxford– AstraZeneca vaccination in Africa range from Int$1,587,020 in the Central African Republic to Int$6,800,921,734 in South Africa. Four (7%) countries had indirect cost savings of less than Int$10 million; 12 (22%) countries had between Int$10 million and Int$50 million; 10 (19%) countries had between Int$50 million and Int$100 million; and 28 (52%) countries had savings of Int$101 million or more. 3.3.3. Savings in Potential Total Costs of COVID-19 in Africa Expected from COVID-19 Vaccination It is estimated that the vaccination of 100% of the eligible population in Africa with the Oxford–AstraZeneca vaccine would potentially prevent 26.22 million COVID-19 infections and avert 4,293,234 deaths. Almost 32.4% of the latter would be among those aged 15–64. As depicted in Table 10, vaccinating all eligible people would save the continent approximately Int$41,624,735,824. Out of these, Int$6.262 billion (15.0%) represents a direct cost savings, and Int$35.363 billion (85.0%) represents an indirect cost savings. Table 10. Savings in potential total costs of COVID-19 expected from COVID-19 vaccination in Africa (in 2021 Int$ or PPP). Country Direct Cost Savings (Int$) Indirect Cost Savings (Int$) Total Cost Savings (Int$) Algeria 802,996,067 1,909,014,416 2,712,010,483 Angola 74,530,213 2,547,771,063 2,622,301,276 Benin 20,318,695 91,972,651 112,291,346 Botswana 49,638,885 650,154,625 699,793,511 Burkina Faso 29,792,819 97,813,488 127,606,307 Burundi 20,939,206 101,436,938 122,376,144 Cameroon 80,384,542 154,472,902 234,857,444 Cape Verde 5,172,902 18,465,553 23,638,455 Central African Republic 50,744,593 1,587,020 52,331,613 Chad 20,021,916 21,143,503 41,165,418 Comoros 2,444,240 20,684,314 23,128,555 Congo, Republic of 5,527,613 63,691,418 69,219,031 Cote d’Ivoire 93,726,056 144,332,155 238,058,211 Democratic Republic of Congo 31,148,801 776,068,275 807,217,076 Equatorial Guinea 16,197,975 32,037,564 48,235,539 Economies 2025,13, 241 34 of 48 Table 10. Cont. Country Direct Cost Savings (Int$) Indirect Cost Savings (Int$) Total Cost Savings (Int$) Eritrea 9,541,700 25,075,786 34,617,486 Ethiopia 157,930,982 1,348,397,948 1,506,328,931 Gabon 20,987,666 94,035,774 115,023,440 Gambia 3,629,006 10,065,227 13,694,232 Ghana 147,748,054 487,962,646 635,710,699 Guinea 35,541,792 63,368,970 98,910,762 Guinea-Bissau 5,292,997 6,376,107 11,669,104 Kenya 276,779,832 3,916,573,442 4,193,353,274 Lesotho 16,490,768 32,258,045 48,748,812 Liberia 5,844,176 23,536,632 29,380,808 Madagascar 32,406,543 823,766,172 856,172,715 Malawi 43,213,865 247,155,826 290,369,691 Mali 34,487,553 83,669,035 118,156,588 Mauritania 20,029,157 54,615,001 74,644,159 Mauritius 42,422,651 508,937,955 551,360,606 Mozambique 87,642,270 399,598,914 487,241,184 Namibia 38,511,526 240,125,016 278,636,543 Niger 37,946,278 77,876,604 115,822,882 Nigeria 1,101,341,861 694,000,964 1,795,342,825 Rwanda 70,703,728 566,789,645 637,493,373 São Tomé and Principe 915,936 3,182,788 4,098,724 Senegal 51,278,667 156,811,935 208,090,601 Seychelles 3,639,156 44,897,125 48,536,282 Sierra Leone 41,019,502 8,528,268 49,547,771 South Africa 319,967,138 6,800,921,734 7,120,888,872 South Sudan 11,230,680 71,660,149 82,890,829 Swaziland 14,781,619 67,988,862 82,770,481 Tanzania 144,727,057 2,643,552,220 2,788,279,277 Togo 22,173,304 18,789,271 40,962,575 Uganda 146,899,253 1,335,323,816 1,482,223,069 Zambia 117,253,509 715,750,036 833,003,545 Zimbabwe 56,431,076 434,709,514 491,140,591 Djibouti 2,612,628 17,403,517 20,016,146 Egypt 980,134,546 4,464,420,175 5,444,554,721 Libya 1,074,816 220,959,103 222,033,918 Morocco 400,408,902 1,157,907,945 1,558,316,848 Somalia 9,372,287 11,154,672 20,526,959 Sudan 208,893,084 339,371,278 548,264,362 Tunisia 236,694,727 514,987,003 751,681,730 TOTAL 6,261,584,816 35,363,151,009 41,624,735,824 Economies 2025,13, 241 35 of 48 6,800,921,734 4,464,420,175 3,916,573,442 2,643,552,220 2,547,771,063 1,909,014,416 1,348,397,948 1,335,323,816 1,157,907,945 823,766,172 776,068,275 715,750,036 694,000,964 650,154,625 566,789,645 514,987,003 508,937,955 487,962,646 434,709,514 399,598,914 339,371,278 247,155,826 240,125,016 220,959,103 156,811,935 154,472,902 144,332,155 101,436,938 97,813,488 94,035,774 91,972,651 83,669,035 77,876,604 71,660,149 67,988,862 63,691,418 63,368,970 54,615,001 44,897,125 32,258,045 32,037,564 25,075,786 23,536,632 21,143,503 20,684,314 18,789,271 18,465,553 17,403,517 11,154,672 10,065,227 8,528,268 6,376,107 3,182,788 1,587,020 - 2,000,000,000 4,000,000,000 6,000,000,000 8,000,000,000 South Africa Egypt Kenya Tanzania Angola Algeria Ethiopia Uganda Morocco Madagascar Congo, Democratic Republic of Zambia Nigeria Botswana Rwanda Tunisia Mauritius Ghana Zimbabwe Mozambique Sudan Malawi Namibia Libya Senegal Cameroon Cote d'Ivoire Burundi Burkina Faso Gabon Benin Mali Niger South Sudan Swaziland Congo, Republic of Guinea Mauritania Seychelles Lesotho Equatorial Guinea Eritrea Liberia Chad Comoros Togo Cape Verde Djibouti Somalia Gambia, The Sierra Leone Guinea-Bissau Sao Tome and Principe Central African Republic International Dollars (Int$) Figure 9. Total indirect cost savings from COVID-19 Oxford–AstraZeneca vaccination in Africa (in 2021 Int$ or PPP). Figure 10 demonstrates that the total cost savings expected from the COVID-19 AstraZeneca vaccination vary from Int$4,098,724 in São Tomé and Principe to Int$7,120,888,872 Economies 2025,13, 241 36 of 48 in South Africa. Twenty-one (39%) countries had a total cost saving of below Int$100 million; 14 (26%) countries had between Int$100 million and Int$499 million; 9 (17%) countries had between Int$500 and Int$999 million; and 10 (18%) countries had Int$1 billion and above. 7,120,888,872 5,444,554,721 4,193,353,274 2,788,279,277 2,712,010,483 2,622,301,276 1,795,342,825 1,558,316,848 1,506,328,931 1,482,223,069 856,172,715 833,003,545 807,217,076 751,681,730 699,793,511 637,493,373 635,710,699 551,360,606 548,264,362 491,140,591 487,241,184 290,369,691 278,636,543 238,058,211 234,857,444 222,033,918 208,090,601 127,606,307 122,376,144 118,156,588 115,822,882 115,023,440 112,291,346 98,910,762 82,890,829 82,770,481 74,644,159 69,219,031 52,331,613 49,547,771 48,748,812 48,536,282 48,235,539 41,165,418 40,962,575 34,617,486 29,380,808 23,638,455 23,128,555 20,526,959 20,016,146 13,694,232 11,669,104 4,098,724 - 2,000,000,000 4,000,000,000 6,000,000,000 8,000,000,000 South Africa Egypt Kenya Tanzania Algeria Angola Nigeria Morocco Ethiopia Uganda Madagascar Zambia Congo, Democratic Republic of Tunisia Botswana Rwanda Ghana Mauritius Sudan Zimbabwe Mozambique Malawi Namibia Cote d'Ivoire Cameroon Libya Senegal Burkina Faso Burundi Mali Niger Gabon Benin Guinea South Sudan Swaziland Mauritania Congo, Republic of Central African Republic Sierra Leone Lesotho Seychelles Equatorial Guinea Chad Togo Eritrea Liberia Cape Verde Comoros Somalia Djibouti Gambia, The Guinea-Bissau Sao Tome and Principe International Dollars (Int$) Figure 10. Total cost saving expected from COVID-19 Oxford–AstraZeneca vaccine in Africa (in 2021 Int$ or PPP). Economies 2025,13, 241 37 of 48 The longer the COVID-19 pandemic persists, the more people develop coping mechanisms that reduce disruptions to activities of daily living (including work), productivity losses, and hence expected savings. Additionally, as the COVID-19 mutation level continues to evolve and milder variants emerge, stability is likely to be achieved, where infection does not result in complete immobility and incapacity in those infected. All these considerations may influence the magnitudes of projected savings from vaccination. 3.4. Sensitivity Analysis First, a re-run of the human capital model with a discount rate of 5% instead of 3%, holding all other variables (deaths, GDPPC, CEHPC, average life expectancy at birth) constant reduced the continental: (i) Total monetary value of reported lives lost to COVID-19 by Int$947,265,868 (14.17%), and the average monetary value per death from Int$47,015 to Int$40,352. (ii) Total productivity loss (total indirect cost) among 15–64-year-olds from Int$3,173,546,125 to Int$2,687,145,189, which is a Int$486,400,935 (15.3%) decrease. (iii) Reported cases total cost (total direct cost plus total indirect cost) from Int$7,155,473,174 to Int$6,669,072,238, which is a reduction of Int$486,400,935 (6.8%). (iv) Projected total savings from COVID-19 vaccination decreases by Int$ 5,340,467,285 (12.83%). Second, a re-estimation of the economic model with a discount rate of 10% instead of 3%, holding all other variables (deaths, GDPPC, CEHPC, average life expectancy at birth) constant, reduced the following in Africa: (i) Total monetary value of reported lives lost to COVID-19 by Int$2,455,027,232 (36.73%), and the average monetary value per death from Int$47,015 to Int$29,746. (ii). Total productivity loss (total indirect cost) from Int$3,173,546,125 to Int$1,919,633,288, which is an Int$1,253,912,837 (39.5%) decrease. (iii) Reported case’s total cost (total direct cost plus total indirect cost) from Int$7,155,473,174 to Int$5,901,560,337, which is a reduction of Int$1,253,912,837 (17.5%). (iv) Projected total savings from COVID-19 vaccination decreases by Int$13,814,723,812 (33.2%). Third, a re-calculation of the model with the world’s highest average life expectancy (ALE) of 88 years (in Hong Kong) instead of individual country’s national ALE, holding all other variable constant (deaths, GDPPC, CEHPC, discount rate at 3%), grew the following in Africa: (i) Total monetary value of reported lives lost to COVID-19 by Int$13,982,454,402 (209.2%); (ii) total productivity loss (total indirect cost) by Int$3,238,491,270 (102%), i.e., from Int$3,173,546,125 (with national ALE) to Int$6,412,037,395 (with world highest ALE); (iii) total cost of reported cases increased from Int$7,155,473,174 (with national ALE) to Int$10,393,964,444 (with the world’s highest ALE), representing a growth of Int$3,238,491,270 (45.3%). (iv) Total cost savings due to vaccination from Int$41,624,735,824 to Int$85,097,353,802, i.e., a 104.4% growth. Fourth, a re-estimation of the model with Africa’s highest average life expectancy (ALE) of 77.5 years (in Algeria) instead of the individual country’s national ALE, holding all other variables constant (deaths, GDPPC, CEHPC, discount rate at 3%), augmented the following in Africa: (i) total monetary value of reported lives lost to COVID-19 by Int$6,074,005,112 (90.9%), i.e., from Int$6,684,101,196 (at national ALE) to Int$12,758,106,309 at Africa’s highest ALE. (ii) The total indirect cost grew from Int$3,173,546,125 (with national ALE) to Int$5,149,685,439 (with Africa’s highest ALE), representing an increase of Int$1,976,139,314 (62.3%). (iii) The total cost of reported cases increased from Int$7,155,473,174 (with national ALE) to Int$9,131,612,487 (with Africa’s highest ALE), which is a growth of Int$1,976,139,314 (27.6%). (iv) Projected total cost savings from COVID-19 vaccination increased from Int$41,624,735,824 to Int$69,119,995,455, which is an Int$27,495,259,631 (66.1%) increase. Economies 2025,13, 241 38 of 48 4. Discussion 4.1. Key Findings 4.1.1. Value of Human Life Losses, Indirect Costs, and Direct Costs Associated with Actual Reported COVID-19 Cases This study estimates the following: (a) The total discounted monetary value of human life losses associated with 142,171 COVID-19 deaths reported in Africa as of 30 June 2021 at Int$6,684,101,196. (b) The discounted monetary value per human life lost was Int$47,015, and the monetary value of human life lost per person in the population was Int$4.88. (c) Approximately 54,210 of 15–64-year-old persons reported dead from COVID-19 in Africa had a total indirect cost of Int$3,173,546,125 (i.e., after adjustment for labor participation rate), and an average of Int$58,542 per life lost (productivity losses). (d) As of 30 June 2021, the 5,514,709 COVID-19 cases reported in Africa had an estimated total direct cost (TDC) of Int$3,981,927,049. Dividing the estimated TDC by the total number of COVID-19 cases reported in Africa yields an average direct cost of Int$722.06 (US$289.53) per case managed. (e) The total cost (direct plus indirect) associated with the actual 5,514,709 COVID-19 cases reported in Africa as of 30 June 2021, was approximately Int$7,155,473,174. The average total cost per COVID-19 case was Int$1309 and Int$5.22 per person in the population. 4.1.2. Savings in Potential/Projected Total Costs of COVID-19 in Africa Expected from COVID-19 Vaccination Findings (a) How many people in the population would be infected with COVID-19 without and with vaccination? Utilizing the risk rates of infection from Voysey et al. (2021), it is projected that approximately 39,608,709 people would be infected by COVID-19 without vaccination (Control Group) compared to 13,390,885 people with vaccination (Treatment Group). Thus, 100% vaccination coverage of eligible persons would potentially avert an estimated 26,217,824 COVID-19 infections in Africa. (b) How many people in the population would die from COVID-19 without and with vaccination? Applying the risk of death reported by Bernal et al. (2021), it is estimated that 5,203,814 people would die without vaccination, vis-à-vis 910,580 deaths with vaccination. (c) How many deaths would be averted by COVID-19 vaccination? An estimated 4,293,234 deaths from COVID-19 would be prevented by vaccination, i.e., 5,203,814 minus 910,580. (d) How many averted COVID-19 deaths would be within the productive age bracket of 15–64 years? Applying the risk of death reported by Bernal et al. (2021), it is estimated that 1,656,615 people in the 15–64 years age bracket would die without vaccination compared to 289,880 with vaccination. Thus, 1,366,735 deaths in the 15–64-year age bracket would be saved with COVID-19 vaccination, i.e., 1,656,615 minus 289,880. (e) We estimate that vaccinating all eligible people in the population would save the African continent approximately Int$41,624,735,824 (i.e., equivalent to 0.61% of Africa’s total GDP in 2021). That total saving consists of Int$6.262 billion (15.0%) in direct cost savings and Int$35.363 billion (85.0%) in indirect cost savings. (f) The benefit–cost ratio of COVID-19 vaccination is 5.8, implying that Africa reaps $6 in return for every $1 spent on COVID-19 vaccination. (g) The average total saving per person in the population is approximately Int$30.4. 4.2. Comparison with Other Studies 4.2.1. A Comparison of Our Estimates with Results from Similar Studies The average discounted monetary value per human life in Africa, at Int$47,015, was lower than in China, at Int$356,203, by a factor of 8, and in Spain, at Int$470,798, by a factor of 10 Economies 2025,13, 241 39 of 48 (Kirigia & Muthuri,2020a,2020b). The differences could be attributed to Africa’s significantly lower GDP per capita compared to that of China and Spain. Differences in demographic structures are also known to have had a significant role, according to Thorbecke (2022). 4.2.2. Comparison of Estimates from Direct and Indirect Cost Studies In Africa, there is a paucity of research into direct and indirect costs associated with COVID-19. Barasa et al. (2021) estimated the direct unit cost for COVID-19 case management for patients at different stages (asymptomatic, mild/moderate, severe, and critical) in Kenya. These authors’ findings were as follows. First, managing asymptomatic COVID-19 at home costs US$226.71 per patient, whereas managing it at a hospital (and isolation) center costs US$764.16 per patient (Barasa et al.,2021). Second, managing mild to moderate COVID-19 at home-based care costs US$226.96, while hospital or isolation center care costs US$764.41 per patient (Barasa et al.,2021). Third, managing severe COVID-19 in the general hospital ward costs US$1494.38 per patient (Barasa et al.,2021). Fourth, managing critical COVID-19 cases in the hospital intensive care unit costs US$7194.07 per patient (Barasa et al.,2021). Ismaila et al. (2021) estimated the cost of clinical management of COVID-19 infection by disease severity level and treatment setting in Ghana. The authors estimated the total direct cost of home management COVID-19 case at US$282; institutional care for a mild case at US$5707; institutional care for a moderate case at US$9952; institutional care for a severe case at US$20,305; and institutional care for a critical case at US$23,382 (Ismaila et al. (2021). The average total cost per COVID-19 case was US$11,925 (Ismaila et al.,2021). The estimates of unit costs in Ghana by Ismaila et al. (2021) are higher than those of Barasa et al. (2021) in Kenya because of the former calculated costs according to the Ministry of Health’s COVID-19 clinical management protocol. Our study estimated that the average direct cost per case managed is Int$722.06 (US$289.53), which is roughly comparable to Barasa et al.’s (2021) unit cost of US$226 per asymptomatic and mildly to moderately ill COVID-19 patient managed using home-based care. However, our estimated direct cost per COVID-19 case-managed of US$289.53 is 3fold, 3-fold, 5-fold, and 25-fold lower than Barasa et al.’s (2021) unit cost per asymptomatic, mild-to-moderate, severe, and critical COVID-19 disease case managed at hospital/isolation center, hospital general inpatient ward, and hospital intensive care unit, respectively. Similarly, whereas our estimated direct cost per COVID-19 case of US$289.53 is comparable to Ismaila et al.’s (2021) total direct cost of home management per COVID-19 case of US$282, it is 41-fold lower than their average total direct cost per COVID-19 case of US$11,925. Comparison with the average total direct costs from Ghana and Kenya implies that our finding may be a gross underestimate and, thus, should be viewed as a lower limit. Limitations of the Study First, like other HCA studies, we used GDP per capita to monetarily value human life losses associated with the COVID-19 pandemic in Africa. GDP per capita suffers several limitations: (a) does not capture home non-marketed production; (b) does not reflect extant inequalities in wellbeing, income, and wealth; (c) omits negative externalities of economic production activities, e.g., environmental pollution, global warming, soil erosion, deforestation, inter-community fights over dwindling water resources (especially in arid and semi-arid areas), depletion of natural resources (and hence accompanying long-term wellbeing sustainability consequences); (d) not an accurate measure of societal happiness and quality of life (Stiglitz et al.,2010). Thus, our calculation yields the lower bound of the monetary value of a life lost to COVID-19. Second, the standard HCA has some perceived shortcomings. One, Landefeld and Seskin (1982) posit that HCA “... is implicitly based upon the maximization of society’s Economies 2025,13, 241 40 of 48 present and future production” (p. 556). Thus, the approach assumes that the only reason society invests, for example, in preventing premature deaths from COVID-19 is to maximize economic production (or GDP). However, as Mooney (1977) argues, there are other reasons, e.g., human life (or health) has intrinsic value (valued for its own sake), society values the life (or health) of its members (productive or not), being alive enables individuals to enjoy leisure. Two, standard HCA attaches zero value for people without income, e.g., retired, children below legal working age, severely handicapped, and homemakers. Three, according to Landefeld and Seskin (1982) application of HCA entails “ . . . choice of an appropriate social discount rate to convert future earnings into present values”, which is contentious (Claxton et al.,2011;Odum et al.,2020). Third, the COVID-19 pandemic may have had broad adverse economic effects on labor markets, corporate investment, and other aspects of the macroeconomy (Campello et al., 2024;Eichenbaum et al.,2021). The economic policies (e.g., cash infusions) implemented by national governments may have contributed partly to mitigating economic losses (Cortes et al.,2022) and stemming the burden of non-fatal disability and fatalities associated with the pandemic. It was beyond the scope of the current study to estimate and incorporate additional costs related to economic policy interventions. The magnitude of additional costs would depend on the labor market effects and labor allocation effects. Concerning labor market effects, the premature deaths from COVID-19 naturally reduce the labor supply. In an environment of reduced labor demand due to pandemic uncertainty (Campello et al.,2024), this results in lower output, revenues, and corporate investment. However, when fiscal and monetary policies support aggregate demand, it may lead to elevated inflation, as evidenced by the recent 2021 wave (Govindarajan et al., 2022). Elevated inflation is a direct economic cost, insofar as it reduces individuals’ real income. In this case, the cost is directly attributed to life losses, resulting in a mismatch between aggregate labor supply and aggregate demand. Regarding labor allocation effects, the pandemic did not affect every African country equally; thus, its impact on labor supply is inherently heterogeneous. In theory, the potential adverse effects of COVID-19 on labor supply could be mitigated by increased labor mobility, which would suggest migratory patterns from countries (or local geographical regions) with relatively low labor shortages to those with relatively high labor shortages. However, the COVID-19-related restrictions, compounded by a growing wave of anti-immigration sentiment, may have prevented the ideal labor relocation (Funke et al.,2023). Fourth, our study utilized the number of COVID-19 cases and deaths reported by individual African countries in estimating the discounted monetary value of human life losses associated with COVID-19 (Section 3.1) and the economic cost of COVID-19 in Africa (Section 3.2). These statistics are likely to be grossly underestimated due to the inadequacy of national health information systems (WHO/AFRO,2012); 6% completeness of cause-ofdeath primary data in the WHO African Region (compared to 97% in the European Region and 94% in the Region of the Americas) (WHO,2019b); and a 52% gap in International Health Regulations core capacities (WHO,2020,2021c). Msemburi et al. (2023) estimated that between January 2020 and December 2021, the total COVID-19 death toll in the African Region was eight-fold higher than the officially reported number of COVID-19-related deaths. Thus, by relying on the cumulative number of COVID-19-related deaths reported by national governments, we may have underestimated the total monetary value of human life losses (in Section 3.1) and the associated indirect costs (in Section 3.1) by almost eight-fold. We chose to use the official national COVID-19related deaths reported by individual WHO Member States, rather than the WHO excess mortality estimates published by Msemburi et al., to increase the likelihood of uptake of the study’s results in public health advocacy by Ministries of Health on the continent. Economies 2025,13, 241 47 of 48 Odum, A. L., Becker, R. J., Haynes, J. M., Galizio, A., Frye, C. C. J., Downey, H., Friedel, J. E., & Perez, D. M. (2020). Delay discounting of different outcomes: Review and theory. Journal of the Experimental Analysis of Behavior,113, 657–679. [CrossRef] Onwujekwe, O., Orjiakor, C., Ogbozor, P., Agu, I., Agwu, P., Wright, T., Balabanova, D., & Kohler, J. (2023). Examining corruption risks in the procurement and distribution of COVID-19 vaccines in select states in Nigeria. Journal of Pharmaceutical Policy & Practice, 16(1), 141. [CrossRef] Organisation for Economic Co-operation and Development [OECD]. (2000). A system of health accounts. Version 1.0 (pp. 1–184). OECD. Petty, W. (1699). Political arithmetick, or a discourse: Concerning the extent and value of lands, people, buildings; husbandry, manufacture, commerce, fishery, artizans, seamen, soldiers; publick revenues, interest, taxes, superlucration, registries, banks. Robert Caluel. Rice, D. P. (2000). Cost of illness studies: What is good about them? Injury Prevention,6, 177–179. [CrossRef] [PubMed] Robinson, L., Hammitt, J., & O’Keeffe, L. (2019). Valuing mortality risk reductions in global benefit-cost analysis. Journal of Benefit-Cost Analysis,10(S1), 15–50. [CrossRef] [PubMed] Rusakaniko, S., Makanga, M., Ota, M., Bockarie, M., Banda, G., Okeibunor, J., Mutapi, F., Tumusiime, P., Nyirenda, T., Kirigia, J. M., & Nabyonga-Orem, J. (2019). Strengthening national health research systems in the WHO African region—Progress towards universal health coverage. Globalization and Health,15, 50. [CrossRef] [PubMed] Shiell, A., Gerard, K., & Donaldson, C. (1987). Cost of illness studies: An aid to decision-making? Health Policy,8, 317–323. [CrossRef] Slothuus, U., & Brooks, R. G. (2000). Willingness to pay in arthritis: A Danish contribution. Rheumatology,39, 791–799. [CrossRef] Statista. (2021a). Distribution of coronavirus (COVID-19) deaths in South Africa as of November 16, 2020, by age. Available online: https:// www.statista.com/statistics/1127280/coronavirus-covid-19-deaths-by-age-distribution-south-africa/ (accessed on 2 June 2021). Statista. (2021b). Coronavirus (COVID-19) mortality rate in Tunisia as of January 2021, by age group (in deaths per 100,000 individuals). Available online: https://www.statista.com/statistics/1203320/covid-19-mortality-rate-in-tunisia-by-age-group/ (accessed on 2 June 2021). Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (Eds.). (2010). Mismeasuring our lives: Why GDP doesn’t add up (pp. 1–176). The New Press. Thabane, L., Mbuagbaw, L., Zhang, S., Samaan, Z., Marcucci, M., Ye, C., Thabane, M., Giangregorio, L., Dennis, B., Kosa, D., Debono, V. B., Dillenburg, R., Fruci, V., Bawor, M., Lee, J., Wells, G., & Goldsmith, C. H. (2013). A tutorial on sensitivity analyses in clinical trials: The what, why, when and how. BMC Medical Research Methodology,13, 92. [CrossRef] Thorbecke, E. (2022). What can Africa learn from a better understanding of the interaction among growth, inequality and poverty in the fight against the COVID-19 pandemic? Research Report, January 6, 2022, Prepared for the AERC Collaborative Project on Addressing Health Financing Gaps and Vulnerabilities in Africa Due to the Covid-19. AERC. United Nations (UN). (2015). Transforming our world: The 2030 agenda for sustainable development (pp. 1–35). General Assembly Resolution A/RES/70/1. UN. Voysey, M., Clemens, S. A. C., Madhi, S. A., Weckx, L. Y., Folegatti, P. M., Aley, P. K., Angus, B., Baillie, V. L., Barnabas, S. L., Bhorat, Q. E., Bibi, S., Briner, C., Cicconi, P., Clutterbuck, E. A., Collins, A. M., Cutland, C. L., Darton, T. C., Dheda, K., Dold, C., . . . Duncan, C. J. A. (2021). Single-dose administration and the influence of the timing of the booster dose on immunogenicity and efficacy of ChAdOx1 nCoV-19 (AZD1222) vaccine: A pooled analysis of four randomised trials. Lancet,397, 881–891. [CrossRef] Wang, W.-C., Fann, F. C.-Y., Chang, R.-E., Jeng, Y.-C., Hsu, C.-Y., Chen, H.-H., Liu, J.-T., & Yen, A. M.-F. (2021). Economic evaluation for mass vaccination against COVID-19. Journal of the Formosan Medical Association,120(S1), S95–S105. [CrossRef] Weisbrod, B. A. (1971). Costs and benefits of medical research: A case study of poliomyelitis. Journal of Political Economy,79, 527–544. [CrossRef] WHO. (2001). Macroeconomics and health: Investing in health for economic development (pp. 1–210). WHO. WHO. (2009). Guide to identifying the economic consequences of disease and injury (pp. 1–132). WHO. WHO. (2019a). Global health expenditure database. Available online: https://apps.who.int/nha/database/Select/Indicators/en (accessed on 17 July 2021). WHO. (2019b). World health statistics overview 2019: Monitoring health for the SDGs, sustainable development goals (pp. 1–120). WHO. WHO. (2020). Global health observatory data repository. International health regulations (2005) monitoring framework, SPAR. Available online: http://apps.who.int/gho/data/node.main.IHRSPAR?lang=en (accessed on 19 December 2020). WHO. (2021a). Global health observatory. UHC service coverage index. Available online: http://apps.who.int/gho/portal/uhc-overview .jsp (accessed on 30 July 2021). WHO. (2021b). Global health observatory data repository. Water, sanitation, and hygiene. Available online: https://apps.who.int/gho/data/ node.main.166?lang=en (accessed on 14 November 2021). WHO. (2021c). World health statistics 2021: Monitoring health for the SDGs, sustainable development goals (pp. 1–121). WHO. WHO/AFRO. (2006). Health financing: A strategy for the African region (pp. 1–16). Document AFR/RC56/10. WHO/AFRO. WHO/AFRO. (2012). The African health observatory: Opportunity for strengthening health information systems through national health observatories (pp. 1–6). Sixty-Second Session Regional Committee for Africa Document AFR/RC62/13. WHO/AFRO. World Bank. (2021). Labour force participation rate, total (% of total population ages 15–64) (modelled ILO estimate). Available online: https://data.worldbank.org/indicator/SL.TLF.ACTI.ZS (accessed on 6 September 2021). Economies 2025,13, 241 48 of 48 Worldometer. (2021). COVID-19 coronavirus pandemic. Last updated: June 30, 2021, 06:03 GMT. Available online: https://www .worldometers.info/coronavirus/?#countries (accessed on 30 June 2021). Zhao, J., HuajieJin, H., Li, X., Zheng, W., Wang, H., & Pennington, M. (2021). Disease burden attributable to the first wave of COVID-19 in China and the effect of timing on the cost-effectiveness of movement restriction policies. Value in Health,24, 615–624. [CrossRef] [PubMed] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). 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