Getting started with epifitter

Kaique S. Alves

2026-04-12

library(epifitter)
library(dplyr)
library(ggplot2)
library(cowplot)
theme_set(cowplot::theme_half_open(font_size = 12))

What epifitter does

epifitter helps analyze plant disease progress curves by combining:

A first epidemic

set.seed(1)

epi <- sim_logistic(
  N = 80,
  y0 = 0.01,
  dt = 10,
  r = 0.12,
  alpha = 0.2,
  n = 5
)

knitr::kable(head(epi), digits = 4)
replicates time y random_y
1 0 0.0100 0.0100
1 10 0.0325 0.0336
1 20 0.1002 0.0851
1 30 0.2699 0.3328
1 40 0.5511 0.5674
1 50 0.8030 0.7770 
ggplot(epi, aes(time, y, group = replicates)) +
  geom_point(aes(y = random_y), shape = 1, color = "#8597a4") +
  geom_line(color = "#15616d", linewidth = 0.8) +
  labs(
    title = "Simulated epidemic",
    x = "Time",
    y = "Disease intensity"
  )

Plot of a simulated epidemic showing replicate observations and the underlying disease progress curve over time.

Fit candidate models

fit <- fit_lin(time = epi$time, y = epi$random_y)
fit
## Results of fitting population models 
## 
## Stats:
##                  CCC r_squared    RSE
## Logistic      0.9988    0.9976 0.1561
## Gompertz      0.9774    0.9558 0.4734
## Monomolecular 0.9313    0.8714 0.6420
## Exponential   0.9161    0.8452 0.6337
## 
##  Infection rate:
##                 Estimate    Std.error      Lower      Upper
## Logistic      0.11931599 0.0009014682 0.11749801 0.12113397
## Gompertz      0.08329917 0.0027332800 0.07778699 0.08881135
## Monomolecular 0.06325781 0.0037064427 0.05578306 0.07073257
## Exponential   0.05605817 0.0036589168 0.04867927 0.06343708
## 
##  Initial inoculum:
##                    Estimate Linearized     lin.SE         Lower        Upper
## Logistic       1.058484e-02 -4.5376913 0.04291847  9.715742e-03  0.011530776
## Gompertz       7.809693e-05 -2.2468144 0.13013015  4.571513e-06  0.000692952
## Monomolecular -1.515280e+00 -0.9223841 0.17646197 -2.590364e+00 -0.762114625
## Exponential    2.690866e-02 -3.6153072 0.17419928  1.893746e-02  0.038235118

fit$stats_all contains the full ranking of candidate models.

knitr::kable(fit$stats_all, digits = 4)
best_model model r r_se r_ci_lwr r_ci_upr v0 v0_se r_squared RSE CCC y0 y0_ci_lwr y0_ci_upr
1 Logistic 0.1193 0.0009 0.1175 0.1211 -4.5377 0.0429 0.9976 0.1561 0.9988 0.0106 0.0097 0.0115
2 Gompertz 0.0833 0.0027 0.0778 0.0888 -2.2468 0.1301 0.9558 0.4734 0.9774 0.0001 0.0000 0.0007
3 Monomolecular 0.0633 0.0037 0.0558 0.0707 -0.9224 0.1765 0.8714 0.6420 0.9313 -1.5153 -2.5904 -0.7621
4 Exponential 0.0561 0.0037 0.0487 0.0634 -3.6153 0.1742 0.8452 0.6337 0.9161 0.0269 0.0189 0.0382

Visualize predictions

plot_fit(fit, point_size = 1.8, line_size = 0.9)

Faceted plot comparing observed disease intensity values with fitted curves from candidate models.

Work with multiple epidemics

epi_a <- sim_gompertz(N = 50, y0 = 0.002, dt = 5, r = 0.10, alpha = 0.2, n = 3)
epi_b <- sim_gompertz(N = 50, y0 = 0.002, dt = 5, r = 0.14, alpha = 0.2, n = 3)

multi_epi <- bind_rows(epi_a, epi_b, .id = "epidemic")

multi_fit <- fit_multi(
  time_col = "time",
  intensity_col = "random_y",
  data = multi_epi,
  strata_cols = "epidemic"
)

knitr::kable(head(multi_fit$Parameters), digits = 4)
epidemic best_model model r r_se r_ci_lwr r_ci_upr v0 v0_se r_squared RSE CCC y0 y0_ci_lwr y0_ci_upr
1 1 Gompertz 0.0997 0.0017 0.0962 0.1032 -1.7976 0.0513 0.9907 0.1575 0.9953 0.0024 0.0012 0.0044
1 2 Monomolecular 0.0671 0.0032 0.0605 0.0737 -0.4718 0.0957 0.9327 0.2939 0.9652 -0.6028 -0.9484 -0.3186
1 3 Logistic 0.1655 0.0090 0.1473 0.1838 -4.3343 0.2650 0.9168 0.8136 0.9566 0.0129 0.0076 0.0220
1 4 Exponential 0.0984 0.0115 0.0751 0.1218 -3.8625 0.3390 0.7042 1.0408 0.8264 0.0210 0.0105 0.0420
2 1 Gompertz 0.1404 0.0018 0.1367 0.1441 -1.7956 0.0537 0.9949 0.1648 0.9974 0.0024 0.0012 0.0045
2 2 Monomolecular 0.1102 0.0036 0.1028 0.1175 -0.6420 0.1069 0.9677 0.3282 0.9836 -0.9003 -1.3632 -0.5281

Next steps