Analysis and Simulation of Plant Disease Progress Curves

Tools for analysis, visualization, and simulation of plant disease progress curves. Includes functions to calculate area-under-the-curve summaries, fit and compare exponential, monomolecular, logistic, and Gompertz models using linear or nonlinear regression, work with single or multiple epidemics, and produce 'ggplot2'-based visualizations. Also includes an experimental powdery mildew dataset for reproducible teaching and research workflows. See Madden, Hughes, and van den Bosch (2007) for background on the epidemiological methods.

epifitter

epifitter provides tools for the visualization, description, and comparison of plant disease progress curves (DPCs). A DPC describes how disease intensity changes over time during an epidemic. By fitting classic population dynamics models such as logistic, monomolecular, Gompertz, and exponential curves, users can compare epidemics and better understand their epidemiological behavior.

epifitter wraps those workflows into a package-oriented interface that includes model fitting, summary measures, simulation helpers, and plotting functions tailored to plant disease epidemiology.

Current functionality includes:

Fit classic population dynamics models using linear and nonlinear approaches
Select models based on statistical and visual analysis
Calculate the area under the disease progress curve
Compare epidemics via visual inference
Simulate synthetic epidemics of various shapes and uncertainty

Why use epifitter?

Compare the same epidemic against multiple canonical disease progress models.
Move from simulation to fitting with a consistent data structure.
Work with single epidemics or grouped data using the same package vocabulary.
Produce ggplot2-ready outputs for reports, papers, and teaching material.

Quick start

Install the stable release from CRAN:

install.packages("epifitter")

Install the development version from GitHub with pak:

if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

pak::pak("AlvesKS/epifitter")

Example

library(epifitter)

set.seed(1)
epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 4)
fit <- fit_lin(time = epi$time, y = epi$y)

plot_fit(fit)

For more complete workflows, see the package articles on model fitting, area summaries, simulation, and the bundled experimental dataset in the documentation site.

Article and citation

epifitter is described in the following paper:

Alves, K. S., & Del Ponte, E. M. (2021). Analysis and simulation of plant disease progress curves in R: introducing the epifitter package. Phytopathology Research, 3, 22. https://doi.org/10.1186/s42483-021-00098-7

If you use epifitter in research, please cite the article above. You can also retrieve the package citation directly in R with citation("epifitter"). A BibTeX entry is shown below:

@article{Alves2021epifitter,
  author = {Alves, Kaique S. and Del Ponte, Emerson M.},
  title = {Analysis and simulation of plant disease progress curves in R: introducing the epifitter package},
  journal = {Phytopathology Research},
  year = {2021},
  volume = {3},
  number = {1},
  pages = {22},
  doi = {10.1186/s42483-021-00098-7},
  url = {https://doi.org/10.1186/s42483-021-00098-7}
}