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

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.