Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves

December 2025, Volume 1, Issue II. doi: 10.65287/josta.202512.72CB Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves Jithin Chandran Kerala Agricultural University Pratheesh P Gopinath* Kerala Agricultural University Pramod R Kerala Agricultural University Aswathy Vijayan Kerala Agricultural University Abstract Temporal analysis of disease progression provides deeper insights into epidemiological patterns by examining disease levels across multiple time points. Disease progress curves (DPCs) capture how disease severity changes over time and reflect the combined effects of host, pathogen, and environment during an epidemic. Mathematical models such as Exponential, Logistic, Monomolecular, and Gompertz are commonly fitted to these curves to quantify epidemic development and compare outbreaks using indicators like fit statistics and parameter estimates. Disease severity may be measured once or repeatedly throughout the epidemic, and quantitative summaries such as Area Under Disease Progress Curves(AUDPC) and Area Under Disease Progress Stairs (AUDPS) help represent the overall disease burden. These metrics support effective comparison among epidemics. R programming, particularly through tools like the epifitter package, enables efficient modeling, visualization, and analysis of DPCs. Overall, disease progress curves are essential in agriculture, helping researchers and epidemiologists to monitor plant health, understand epidemic behavior, and improve disease management strategies. Keywords: Disease progress curves, Temporal disease analysis, Mathematical model fitting, R programming, AUDPC. 2Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves 1. Introduction The challenge of maintaining food security has been increasingly affected by unpredictable climate changes and demanding crop environments. These persistent conditions lead to uncontrollable outbreaks of severe crop diseases over extended periods. Biotic constraints pose a significant threat to crop growth, impacting production and ultimately food security. These constraints often induce plant diseases that progress in a non-linear pattern, emphasizing the importance of timely intervention for effective management. A comprehensive investigation into underlying epidemics can significantly influence disease management practices. Employing suitable models to represent disease progression and predict its future development can greatly assist researchers in devising prompt management strategies. Analyzing the patterns, causes, and effects of diseases in plant populations is the central focus of epidemiological studies (Zadoks and Schein 1980;Van der Plank 1963). Examining disease progression curves helps researchers understand the timing of outbreaks and spread, ultimately leading to improved forecasting and management practices (Campbell and Madden 1990;Madden et al. 2007). With recent improvements in statistical modeling and computational tools, analyzing disease progress curves has become more accurate and detailed. Applying non-linear regression models and temporal analysis has enhanced disease forecasting and risk assessment (Jeger 2004). Despite these advancements, there remains a need for comprehensive studies that integrate various epidemiological factors into a unified framework for specific plant diseases (Kranz 2003; Xu 2006). The disease progress curve is an essential tool for visually representing the proportion of diseased plants, providing a clear measure of a disease’s advancement. This curve effectively illustrates the influence of epidemiological factors and epidemic components, assisting researchers in identifying underlying epidemics (Campbell and Madden 1990;Madden et al. 2007). Through the incorporation of appropriate mathematical models and thorough analysis of temporal progress, disease severity can be accurately quantified, enabling assessment of epidemic development and prediction of future disease trajectories (Van der Plank 1963;Hau and Kranz 1990). Employing statistical tools and visualization techniques is crucial for obtaining valuable insights into disease development and offers a convenient means of interpreting causal factors, thereby facilitating the timely implementation of effective management strategies to maintain healthy field conditions (Nutter Jr and Schultz 1995;Jeger and Viljanen-Rollinson 2001). The interconnected nature of epidemiology, disease management, and global drivers such as climate change and trade is conceptually illustrated in Figure 1. This framework highlights how surveillance, diagnostics, and management strategies are shaped by evolving environmental and economic pressures, reinforcing the need for integrated approaches in plant pathology. A better understanding of the epidemic process is imperative for implementing an effective control strategy, as epidemiology and disease management are intertwined yet distinct and inseparable facets of plant pathology (Jeger 2004). Therefore, establishing an epidemiological framework using classical models and progress curves aims to target, enhance, and deploy methods that mitigate the risk of disease incidence in crops. Recently, (Savary and Willocquet 2020), has reinforced this need, showing that integrating epidemiological modelling with modern data driven approaches significantly improves disease forecasting and management outcomes in agricultural systems. Journal of Sustainable Technology in Agriculture 3 Figure 1: Multi-disciplinary approach in plant disease management 2. Nature of epidemics Agriculture frequently results in simplified ecosystems in which humans alter the natural equilibrium between plants and pathogens. In this situation, diseases can develop into severe epidemics (González-Domínguez et al. 2020). The damage and loss incurred by crops are direct results of disease development, which occurs due to favorable conditions for initial infection and subsequent progression. These impacts and losses can be presented as the functions of disease progress. Plant disease epidemiology evolves by: a pathogen population, a plant host population, their environment, and human actions (Savary and Willocquet 2020). The quantification of derived functions is essential to comprehend disease progression and the factors impacting it. A thorough understanding of disease dynamics is important in epidemiological research, facilitating the timely implementation of management strategies through the visualization of disease progress curves. 2.1. Cyclic nature of plant disease Plant disease epidemics exhibit a cyclic nature, characterized by recurring phases of pathogen development driven by interactions among the host, pathogen, and environment. The epidemic begins when inoculum-such as fungal spores, bacterial cells, nematodes, or virus particles transmitted by vectors-initiates infection and becomes established within susceptible host tissues. As the pathogen colonizes and multiplies within the host, it produces new inoculum capable of dispersing to additional infection sites, thereby sustaining the epidemic cycle. Diseases in which pathogens complete only one infection cycle during a crop cycle are classified as monocyclic, whereas pathogens capable of generating repeated infection cycles within the same season are described as polycyclic (Van der Plank 1963;Campbell and Madden 1990). Polycyclic pathogens often drive rapid epidemic development due to exponential inoculum build-up, while monocyclic pathogens progress more linearly, with epidemic intensity closely tied to the initial inoculum level (Madden et al. 2007;Del Ponte 2023). Understanding these cyclic processes is fundamental for predicting epidemic dynamics and implementing timely 4Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves disease management strategies. 3. Disease assessment Disease assessment is the foundational step in plant disease epidemiology because every subsequent analysiswhether estimating infection rates, fitting disease progress models, or comparing treatmentsdepends on the accuracy and consistency of the initial measurements. The process begins with the identification of characteristic symptoms and signs, which requires familiarity with the pathogen, host physiology, and environmental conditions that influence symptom expression. (Campbell and Neher 1994) emphasized that although specific assessment techniques may vary depending on study objectives, the overarching goal remains constant: to obtain reliable, repeatable, and cost‑effective estimates of disease presence and intensity with known confidence. A comprehensive disease assessment typically involves several sequential components. First, the sampling strategy must be defined, including the number of plants or plant parts to be evaluated, the spatial pattern of sampling within the field, and the timing of assessments relative to crop growth stages. Proper sampling ensures that the collected data accurately represent the true disease status of the population and minimizes bias caused by uneven disease distribution. the measurement of disease intensity is carried out using one or more standardized metrics. Disease intensity refers to the amount of disease within a defined area or population (Seem 1984). Within this framework, it is essential to distinguish between its major components: •Disease incidence: which quantifies the proportion or number of plant units showing visible symptoms. It is particularly useful for diseases that produce discrete, easily identifiable symptoms. •Disease severity: which measures the proportion of plant tissue affected, often expressed as a percentage of the total tissue area or volume (Kranz 1974). Severity is more informative for diseases that cause continuous damage, such as foliar blights or rusts. In some cases, disease prevalence is also assessed, representing the proportion of fields or geographic units in which the disease occurs (Zadoks and Schein 1980). This metric is especially valuable for regional surveillance and risk mapping. Following measurement, the data must be recorded, validated, and standardized. This may involve the use of categorical scales, diagrammatic keys, digital imaging tools, or quantitative laboratory methods, depending on the disease and study objectives. Standardization reduces assessor bias and enhances comparability across observers, locations, and time points. Finally, the assessed data are summarized and interpreted to characterize the epidemic. These summaries form the basis for constructing disease progress curves, estimating epidemiological parameters, comparing treatments, and informing management decisions. Accurate disease assessment therefore not only describes the current status of disease but also enables robust modelling, forecasting, and evaluation of control strategies. Journal of Sustainable Technology in Agriculture 5 3.1. Methods of disease assessment Disease assessment in crops is a fundamental component of plant pathology, serving as the basis for quantifying epidemic intensity, comparing treatments, evaluating host resistance, and developing reliable disease progress curves. Crop disease severity can be quantified through several methodological approaches that differ in precision, scalability, and practicality. Traditional visual assessment conducted by trained observers remains the most widely adopted technique due to its simplicity and suitability for field conditions; however, it is inherently subjective and susceptible to assessor bias, even when standardized scales are used (Nutter Jr and Schultz 1995;Bock et al. 2010). Advancements in imaging technologies have enabled the development of remote sensing-based assessment methods, including RGB imaging, multispectral imaging (MSI), and hyperspectral imaging (HSI), which significantly enhance objectivity and consistency. RGB cameras capture high-resolution colour imagery suitable for detecting visual symptoms, while MSI records reflectance in selected spectral bands associated with plant physiological responses and stress. HSI, providing detailed spectral signatures across numerous contiguous wavelengths, supports early detection of subtle biochemical and structural changes preceding visible symptom expression (Mahlein 2016;Mahlein et al. 2018). In addition to imaging-based approaches, contact-based measurement methods-such as manual lesion counting, digital area measurement tools, planimetry, and laboratory-based quantification of pathogen biomass using qPCR or ELISA-offer high accuracy but are often labour-intensive and time-consuming (Bock et al. 2010;Mutka and Bart 2015). Collectively, these methods form a continuum from simple field-based visual scoring to advanced sensor-driven analytics, allowing researchers to select the most appropriate approach based on the scale, objectives, and precision requirements of disease monitoring in agricultural systems (Del Ponte 2023). These diverse approaches to disease quantification—from visual scoring to advanced spectral imaging and contact-based measurements—are conceptually illustrated in Figure 2, which categorizes remote sensing and direct measurement techniques based on their mode of interaction with plant material. Figure 2: Overview of crop disease assessment methods, including remote sensing techniques (visual, RGB, multispectral, and hyperspectral imaging) and contact-based measurements (source:Del Ponte and Esker (2008)) 6Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves A good-quality disease assessment should be reliable, meaning that it consistently reflects the true level of disease within a crop population. Reliability encompasses three key attributes: accuracy, precision, and reproducibility (Nutter Jr and Schultz 1995;Bock et al. 2010). Accuracy refers to the closeness of the sample mean to the true population mean, indicating how well an assessment represents actual disease levels. Precision, in contrast, measures how closely repeated estimates cluster around their mean value, commonly quantified using the sample variance (S²). A larger S² indicates lower precision, reflecting greater variability among assessment values; such variability may arise from assessor error or genuine heterogeneity in disease distribution among plants (Bock et al. 2010). Understanding the sources of variation is essential, as biological factors-such as localized pathogen spread or microenvironmental differences-can also contribute significantly to observed variance. Reproducibility is another critical component of reliable disease quantification and refers to an evaluator’s ability to consistently assign similar disease severity estimates when repeating assessments on the same plants or plots within a short period. One widely used approach to evaluate reproducibility involves correlation analysis between two consecutive assessments of identical units. A high correlation coefficient (r� 0.80) is generally considered indicative of strong reproducibility, suggesting that the assessment protocol produces consistent and dependable results (Nita et al. 2003;Nutter Jr and Schultz 1995). Ensuring high reproducibility is fundamental, as it increases confidence in treatment comparisons, model fitting, and epidemiological interpretations. In practical disease assessment, accuracy, precision, and reproducibility are achieved through the use of standardized rating scales, assessor calibration exercises, and repeated or replicated measurements, all of which reduce observer related variation and ensure that disease estimates reliably reflect true epidemic conditions. 4. Modeling the epidemics The main goal of epidemiological research is to comprehend the correlation between disease patterns and external influences. The progression of a disease over time can be represented through appropriate models, often depicted as curves. Enhanced understanding can facilitate the creation of more effective, sustainable, and efficient management strategies to mitigate the impact of diseases on crop yield. The relationship between epidemic development and crop yield loss can be more accurately quantified by integrating disease progress with the timing of infection events. Traditional methods often rely solely on final disease severity or cumulative disease intensity; however, the timing of pathogen establishment has a critical influence on the final yield outcome. The framework proposed by the 1998 study on coupling disease-progress curves with time-of-infection functions demonstrated that early infections disproportionately reduce yield because they allow more time for pathogen colonization and physiological disruption of host tissues (Madden et al. 2000). 4.1. Disease progress curves Disease intensity - expressed as incidence, prevalence, or severitywhen monitored over time, produces disease progress curves (DPCs), which serve as fundamental tools for quantifying the temporal dynamics of plant disease epidemics. These curves capture how diseases evolve within a host population and allow researchers to analyze epidemic patterns using established Journal of Sustainable Technology in Agriculture 7 growth models that reflect biological processes of infection, colonization, and host response (Jeger 2004;Madden et al. 2007). Depending on the pathogen’s life strategy and environmental interactions, DPCs typically follow mathematical forms such as monomolecular, logistic, Gompertz, or exponential curves as illustrated in Figure 3, each corresponding to unique epidemic behaviors and contributing factors (Dar et al. 2021;Esker et al. 2013). Quantitative analysis of these curves provides two critical epidemiological parameters: the initial inoculum level (y�) and the apparent infection rate (r), both of which help characterize epidemic onset and progression (Nutter Jr et al. 2015;Jeger and Viljanen-Rollinson 2001). These parameters enable comparisons across cultivars, treatments, environments, and cropping systems, making them essential in evaluating the effectiveness of cultural practices, host resistance, and chemical or biological control measures (Del Ponte and Esker 2008;Esker et al. 2013). In addition to describing disease development, DPCs also hold substantial predictive value. Because disease severity patterns often correlate with future epidemic intensity, fitted progress curves can be used to forecast disease trajectories, epidemic thresholds, and potential yield impacts under varying environmental scenarios (Duku et al. 2016;Bock et al. 2020). This predictive capacity supports the development of early-warning systems and disease risk models that guide timely and effective management interventions, particularly in the context of climate variability and precision agriculture (Garrett et al. 2013;Mahlein 2016). Thus, disease progress curves serve not only as descriptive epidemiological tools but also as a foundation for forecasting, decision support, and sustainable plant disease management. Figure 3: Classic models in disease progress curves Disease dynamics are measured using disease intensities y(t), which can be depicted as summation curves or rate curves. A Disease Progression Curve (DPC) provides a summary of the interaction among the three primary components of the disease triangle during an epidemic. The shapes of the curves can exhibit significant variations based on the individual characteristics of each component. These characteristics can be influenced by management practices aimed at altering the trajectory of the epidemic, with the ultimate objective of restraining the progression of the disease. The disease progress curve illustrated in Figure 4was generated using a model dataset representing disease severity recorded across successive time points (days). The visualization was produced using the R statistical computing environment (RStudio), which enabled the construction of a representative epidemic trajectory for demonstrating typical patterns of 8Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves Figure 4: Disease progress curve(model) temporal disease development. 4.2. Classifiaction of epidemics The morphology of disease progress curves (DPCs) exhibits significant variability depending on the type of crop disease. This diversity can be categorized into two primary groups: monocyclic and polycyclic (Del Ponte 2023). Monocyclic diseases involve disease progression and sustenance solely through the primary inoculum, with no secondary infection occurring during the crop cycle. Classic examples include common smut of maize (Ustilago maydis) and Fusarium wilt of banana (Fusarium oxysporum f. sp. cubense), both of which typically produce saturation‑type progress curves. In contrast, polycyclic diseases generate secondary inoculum within the same season, enabling repeated infection cycles. Well‑known examples include late blight of potato (Phytophthora infestans) and powdery mildew of cereals (Blumeria graminis), which characteristically produce sigmoid‑shaped progress curves due to rapid, exponential epidemic development. These distinct curve morphologies are visually represented in Figure 5. The principal aim of epidemiological research is to decrease the incidence of disease below a specific threshold. Therefore, in order to comprehensively reduce the progression of diseases, it is imperative to develop distinct mathematical models for each type of disease. Monocyclic disease is primarily concerned on the amount of inoculum present at the begin of the infection stage(initial inoculum). The initial inoculum amount (Q1) at the beginning of the current season is the sum of the initial inoculum at the beginning of the previous season (Q0) and the increment resulting from the pathogen’s growth and development during the season as represented in Equation 1. 𝑄1= 𝑄0+ 𝑖𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡 (1) The increment is directly proportional to the amount of last season’s initial inoculum. It can be approximated as a simple proportion of last season’s initial inoculum, KQ0, where K is a proportionality constant (Equation 2). Journal of Sustainable Technology in Agriculture 9 Figure 5: Hypothetical curves for monocyclic (left) and polycyclic (right) epidemics(source:Del Ponte (2023)) 𝑄1= 𝑄0+ 𝐾𝑄0(2) The K represents all the factors affecting the growth of the pathogen dispersal of inoculum and other factors contributing to the increase in inoculum. The value of K depends on various factors such as environmental conditions, cultivation practices, crop development. The value of K depends on the nature of disease progress , it will be positive and there is a net increase from one session to next . On the other hand if there is a net decrease in the production of inoculum, K would be negative, this situation occurs during rotation with non host crops. In order to describe the changes in the initial inoculum from one season to the next in a polyetic epidemic, we will generalize the subscript that indicates the season (Equation 3). 𝑄𝑇+1 = 𝑄𝑇+ 𝐾𝑄𝑇(3) Figure 6: Disease progression quantified 16 Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves (1) fitting and ranking for the models based on summary stats. (2) comparing model parameters. (3) calculating the area under the disease progress curve. (4) plotting diagnostic and publication-ready plots via customization of ggplot2 objects. Figure 9: Logo of epifitter package(source:Alves and Del Ponte (2021)) 7.3. Workflow of epifitter package Once the epidemic dataset is collected, its imported in R studio and further analysis is done using the pre installed epifitter package. The built in functions for Model fitting (fit_lin, fit_nlin, and fit_nlin2) is used to fit the epidemic dataset into different statistical model to identify the best fit model that describes the overall epidemiological process. For making a simulated dataset of the disease severity rating with time, four functions (sim_logistic, sim_exponential, sim_monomolecular, sim_gompertz) specifically designed for the four statistical models are used. The further analysis and visualization can be done using the simulated dataset. For comparative study between different treatments and variables in the disease progression studies, a quantitative summary of the overall epidemiology can be understood using the Area Under Disease Progress Curves (AUDPC()), and its mathematical modification Area under Disease Progress Stairs (AUDPS()) function can be used. These two functions provide a quantitative summary on the disease progress (Alves and Del Ponte 2021). The complete analytical pipeline of the epifitter packagefrom data input to model fitting, visualization, and summary, is illustrated in Figure 10. This workflow diagram provides a structured overview of the package’s core functions and their sequential application in epidemic data analysis. A key limitation of the epifitter package is that its modelling accuracy depends on sufficiently detailed and regularly spaced epidemic observations, and the assumptions embedded in each growth model may reduce reliability when datasets are sparse, Journal of Sustainable Technology in Agriculture 17 irregular, or highly variable. Figure 10: Workflow of epifitter package(source:Alves and Del Ponte (2021)) Function Description fit_lin() Fits models to single DPC data via linearization fit_nlin() Fits models to single DPC via nonlinear regression fit_nlin2() An extension of fit_nlin() that allows estimating the maximum asymptote parameter 𝐾 fit_multi() Fits models to multiple DPCs using either linear or nonlinear regression plot_fit() Generates ggplot2 visualization of the output from a model-fitting object sim_exponential() Simulates DPC using the exponential model sim_monomolecular() Simulates DPC using the monomolecular model sim_logistic() Simulates DPC using the logistic model sim_gompertz() Simulates DPC using the Gompertz model AUDPC() Calculates the area under the disease progress curve AUDPS() Calculates the area under the disease progress stairs Table 2: Functions in epifitter package 8. AUDPC Disease severity in any plant-patho system can be assessed either once at the peak of the epidemic or several times at some intervals starting from disease initiation until the end of the epidemic. The former method of assessment measures the cumulative effects of all 18 Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves the factors operating during the course of epidemic viz. the terminal disease severity scores (TDS), while the latter can be used to estimate different parameters like the area under the disease progress curves (AUDPC) (Mukherjee et al. 2010). The area under the disease progress curve (AUDPC) is a useful quantitative summary of disease intensity over time, for comparison across years, locations, or management tactics. The most commonly used method for estimating the AUDPC, the trapezoidal method (Figure 11), is to discretize the time variable (hours, days, weeks, months, or years) and calculate the average disease intensity between each pair of adjacent time points (Madden et al. 2007). The AUDPC summarizes the “total measure of disease stress” and is largely used to compare epidemics (Jeger and Viljanen-Rollinson 2001). In the context of disease progression analysis, we can delineate the time points as a sequence (ti), where the temporal intervals between consecutive points may exhibit either uniformity or variability. Concurrently, we are presented with corresponding disease severity metrics (yi). Here, we establish y(0) = y0as the baseline infection or disease level at t = 0, denoting the initial observation of disease severity in our investigation. A(tk), referred to as the Area Under the Disease Progression Curve (AUDPC) at t = tk, represents the cumulative disease severity up to t = tk, and is formulated as the integral of the disease severity over the time period (Mukherjee et al. 2010). Figure 11: (source:Simko and Piepho (2012)) AUDPC =𝑛−1 ∑ 𝑖=1 (𝑦𝑖+ 𝑦𝑖+1) 2(𝑡𝑖+1 − 𝑡𝑖)(10) yi: Assessment of a disease (percentage, proportion, ordinal score, etc.) at the ith observation ti: Time (in days, hours, etc.) at the ith observation n : Total number of observations. This approach of summarising disease progress data into one value is appropriate when damages to host are proportional to the total amount and duration of the disease. When observed disease patterns can be fitted satisfactorily to a model, then AUDPC can be directly obtained from the model intergated over time (Jeger and Viljanen-Rollinson 2001). When we compare different epidemics, it may be necessary to standardise AUDPC values in order to take into account the fact that epidemics may differ in their lengths of duration. A standardised AU- Journal of Sustainable Technology in Agriculture 19 DPC value is obtained by dividing the AUDPC by the total duration time and sometimes by the integration interval (t). 9. AUDPS The first observation typically indicates the initial level of disease, while the last observation reflects the final extent of the disease at the end of the assessment period. Both of these points are crucial for understanding the full trajectory of disease progression. If these observations are undervalued, the overall assessment may not accurately reflect the severity or impact of the disease over time. The AUDPS method addresses the limitations of AUDPC by giving greater weight to the first and last observations in a disease assessment, which are often undervalued in the AUDPC calculation. This improvement, which results in a better estimation of the overall impact of a disease over time, is particularly important in studies of plant-pathogen interactions, where understanding the full extent of disease progression is essential for effective management (Simko and Piepho 2012). 10. Conclusion Disease progress curves (DPCs) - derived from repeated measurements of disease incidence or severity over time - are invaluable tools in agricultural epidemiology that encapsulate the dynamic interplay of host, pathogen, and environment throughout an epidemic. By fitting classical growth-curve models such as Exponential, Monomolecular, Logistic, or Gompertz to these temporal data, researchers can obtain biologically meaningful parameters (e.g., initial inoculum level, infection rate) that facilitate rigorous comparisons between epidemics under different conditions or management strategies. The use of summary metrics such as AUDPC and AUDPS further enables succinct quantification of overall disease burden supporting comparisons across seasons, treatments, or cultivars. The integration of open-source statistical software such as epifitter in R - which streamlines model fitting, simulation, visualization, and summary calculation - greatly enhances accessibility, reproducibility, and interpretability of disease‐progress analyses. Nevertheless, to fully realize the potential of DPC-based epidemiology in modern agroecosystems, future work should strive for more comprehensive frameworks that combine classical modeling with emerging assessment technologies (e.g., remote sensing, high-throughput phenotyping) and account for environmental variability, host genetic heterogeneity, and management interventions. 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Journal of Sustainable Technology in Agriculture 23 ĺPublication & Reviewer Details Publication Information •Submitted: 10 December 2025 •Accepted: 17 December 2025 •Published (Online): 19 December 2025 Reviewer Information •Reviewer 1: Anonymous •Reviewer 2: Anonymous ĹDisclaimer/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 the publisher and/or the editor(s). The publisher and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © Copyright (2025): Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits non-commercial use, sharing, and reproduction in any medium, provided the original work is properly cited and no modifications or adaptations are made. 24 Modeling Plant Disease Epidemics: A Comprehensive Review of Disease Progress Curves Affiliation: Jithin Chandran Agricultural Statistics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] Pratheesh P Gopinath* Agricultural Statistics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] URL: https://kau.in/people/sub-lt-dr-pratheesh-p-gopinath Pramod R Plant Pathology College of Agriculture, Vellanikkara Thiruvananthapuram, Kerala India E-mail: [email protected] Aswathy Vijayan Agricultural Economics College of Agriculture, Vellayani Thiruvananthapuram, Kerala India E-mail: [email protected] URL: https://kau.in/people/dr-aswathy-vijayan Journal of Sustainable Technology in Agriculture https://www.jostapubs.com/ PAPAYA Academic Press, Statoberry LLP, https://www.statoberry.com/papaya December 2025, Volume 1, Issue II Submitted: 2025-12-10 doi:10.65287/josta.202512.72CB Accepted: 2025-12-17