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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.