Analysis and simulation of plant disease progress curves in R: introducing the epifitter package

Abstract

AbstractThe analysis of the disease progress curves (DPCs) is central to understanding plant disease epidemiology. The shape of DPCs can vary significantly and epidemics can be better understood and compared with an appropriate depiction and analysis. This paper introduces epifitter, an open-source tool developed in R for aiding in the simulation and analysis of DPC data. User-level functions were developed and their use is demonstrated to the reader using actual disease progress curve data for facilitating the conduction of several tasks, including (a) simulation of synthetic DPCs using four population dynamics models (exponential, monomolecular, logistic, and Gompertz); (b) calculation of the areas under disease progress curve and stairs; (c) fitting and ranking the four above-mentioned models to single or multiple DPCs; and (d) generation and customization of graphs. The package requires the installation of R in any desktop computer and the scripted analysis can be fully documented, reproduced, and shared. The epifitter R package provides a flexible suite for temporal analysis of epidemics that is useful for both research and teaching purposes.