Week 4, Session 4 — SIR and SEIR models with deSolve

Course 3 — #courses

R. Heller

Note

Workflow lab: Goal → Approach → Execution → Check → Report.

Learning objectives

• Write a compartmental SIR / SEIR model as a system of ODEs.
• Solve it with deSolve::ode and plot the trajectories.
• Compute the basic reproduction number R₀ from model parameters and illustrate its effect.

Prerequisites

Basic calculus intuition and comfort with differential-equation notation.

Background

Compartmental models partition a population into states — Susceptible, Exposed, Infectious, Recovered — and describe flows between them with ordinary differential equations. The SIR model has three compartments and two parameters (transmission rate β, recovery rate γ); the SEIR model adds an exposed-but-not-yet-infectious class and a progression rate σ. These models are caricatures, but they capture the shape of an epidemic — exponential rise, peak, decline — surprisingly well, and they make the role of R₀ = β/γ visible.

The real power of compartmental models in practice is not prediction but counterfactual reasoning: what if we had vaccinated half the population on day zero, or what if the infectious period were a day shorter? Tuning β and γ to reflect interventions is the bread and butter of public-health modelling.

Setup

library(tidyverse)
library(deSolve)
set.seed(42)
theme_set(theme_minimal(base_size = 12))

1. Goal

Simulate an SIR outbreak in a population of 1 million, for R₀ values 1.5, 2.5, and 4.

2. Approach

sir <- function(time, state, params) {
  with(as.list(c(state, params)), {
    N  <- S + I + R
    dS <- -beta * S * I / N
    dI <-  beta * S * I / N - gamma * I
    dR <-  gamma * I
    list(c(dS, dI, dR))
  })
}

3. Execution

run_sir <- function(R0, gamma = 1/7, days = 180, N = 1e6, I0 = 10) {
  params <- c(beta = R0 * gamma, gamma = gamma)
  state  <- c(S = N - I0, I = I0, R = 0)
  times  <- seq(0, days, by = 1)
  out <- as.data.frame(ode(state, times, sir, params))
  out$R0 <- R0
  out
}

trajectories <- bind_rows(
  run_sir(1.5), run_sir(2.5), run_sir(4.0)
) |>
  pivot_longer(c(S, I, R), names_to = "compartment", values_to = "n")

ggplot(trajectories, aes(time, n, colour = compartment)) +
  geom_line(linewidth = 0.8) +
  facet_wrap(~ R0, labeller = labeller(R0 = \(x) paste0("R0 = ", x))) +
  labs(x = "Day", y = "People")

4. Check

Peak prevalence and attack rate should rise with R₀.

trajectories |>
  filter(compartment == "I") |>
  group_by(R0) |>
  summarise(peak_I = max(n), peak_day = time[which.max(n)]) |>
  arrange(R0)

# A tibble: 3 × 3
     R0  peak_I peak_day
  <dbl>   <dbl>    <dbl>
1   1.5  63023.      145
2   2.5 233307.       56
3   4   403070.       30

5. Report

An SIR compartmental model was simulated for a population of one million with a 7-day mean infectious period across three basic reproduction numbers (R₀ = 1.5, 2.5, 4). Peak prevalence and the final attack rate increased sharply with R₀, illustrating how small changes in transmission translate into large changes in epidemic size. Calibrated analogues of this model underpin much real-world outbreak forecasting.

Note that β and γ are not directly observed; they are inferred from case counts, and the uncertainty in R₀ propagates from that inference.

Common pitfalls

• Interpreting SIR trajectories as forecasts rather than scenarios.
• Forgetting that a constant β across time ignores interventions.
• Comparing peak prevalence across parameter settings without holding N and I₀ constant.

Further reading

• Keeling MJ, Rohani P. Modeling Infectious Diseases in Humans and Animals.
• Soetaert K et al. Solving Differential Equations in R.

Session info

sessionInfo()

R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_Germany.utf8  LC_CTYPE=English_Germany.utf8   
[3] LC_MONETARY=English_Germany.utf8 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.utf8    

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] deSolve_1.42    lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0  
 [5] dplyr_1.2.1     purrr_1.2.2     readr_2.2.0     tidyr_1.3.2    
 [9] tibble_3.3.1    ggplot2_4.0.3   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       jsonlite_2.0.0     compiler_4.5.2     tidyselect_1.2.1  
 [5] scales_1.4.0       yaml_2.3.12        fastmap_1.2.0      R6_2.6.1          
 [9] labeling_0.4.3     generics_0.1.4     knitr_1.51         htmlwidgets_1.6.4 
[13] pillar_1.11.1      RColorBrewer_1.1-3 tzdb_0.5.0         rlang_1.2.0       
[17] stringi_1.8.7      xfun_0.57          S7_0.2.2           otel_0.2.0        
[21] timechange_0.4.0   cli_3.6.6          withr_3.0.2        magrittr_2.0.4    
[25] digest_0.6.39      grid_4.5.2         hms_1.1.4          lifecycle_1.0.5   
[29] vctrs_0.7.3        evaluate_1.0.5     glue_1.8.1         farver_2.1.2      
[33] rmarkdown_2.31     tools_4.5.2        pkgconfig_2.0.3    htmltools_0.5.9