epiworld Workshop - InsightNet 2026

library(epiworldR)

# Create a model
mymodel <- ModelSEIRCONN(
  name = "Nasty virus", 
  n = 10000, 
  prevalence = .001, 
  contact_rate = 4,
  transmission_rate = .4, 
  incubation_days = 7, 
  recovery_rate = 1/7
) |> verbose_off() 

# Add a second virus
add_virus(
  mymodel, 
  virus(
    name = "Nasty virus 2",
    prob_infecting = .5,
    recovery_rate = .3,
    ), 
  proportion = .001
)

# Rename the model
set_name(mymodel, mname = "SEIR COVID-19 model")

# Run the model
set.seed(8383)
run(mymodel, ndays = 100)

# Plot the model
# - Set graphical parameters to plot in 2x2 grid
op <- par(mfrow = c(2, 2), mar = par()$mar * c(1, 1, 1/2, 0))
# - Plot
plot(mymodel)
plot_reproductive_number(mymodel)
plot_incidence(mymodel)
plot_generation_time(mymodel)
# - Restore original graphical parameters
par(op)

Figure 1

Welcome to the InsightNet 2026 workshop Simulating Complex Agent-Based Models with {epiworldR}.

The {epiworldR} R package is a wrapper of the C++ library epiworld. It provides a fast, highly customizable framework for building network-based transmission/diffusion agent-based models (ABM).

Key features of {epiworldR} include:

• Fast simulation with an average of 30 million agents/model-step/second
• Multi-disease models (e.g., competing pathogens, multiple rumors, etc.)
• Mutating pathogens
• User-defined interventions (events, tools, policies, etc.) that change model behavior while it is running
• Adapting transmission and recovery rates based on comorbidities
• Parallel computing out-of-the-box
• Likelihood-free Markov chain Monte Carlo (LFMCMC)
• Models with an arbitrary number of compartments/states beyond SIR/SEIR.

Additionally, while {epiworldR} was built for models of disease transmission, the {epiworld} framework is general enough to build models for many diffusion processes beyond infectious disease.

Requirements

You can install the required packages for the session using the following code:

# The measles and epiworldR packages
install.packages(
  c('epiworldR', 'measles'),
  repos = c(
    'https://uofuepibio.r-universe.dev',
    'https://cloud.r-project.org'
))

# Additional packages
install.packages(c("data.table", "ggplot2", "DiagrammeR", "netplot", "igraph"))