EpiAwareR

EpiAwareR is an R interface to the Julia-based EpiAware compositional infectious disease modelling framework. It enables R users to build flexible epidemiological models by composing reusable components without requiring Julia expertise.

Installation

Prerequisites

EpiAwareR requires:

  • R >= 4.0.0
  • Julia >= 1.9.0 (automatically installed if not present)

Install from GitHub 

# Install devtools if needed
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}

# Install EpiAwareR
remotes::install_github("sbfnk/EpiAwareR")

Setup

On first use, EpiAwareR will attempt to automatically install Julia and required packages. If this fails, run:

Quick Start

Here’s a basic example replicating Mishra et al. (2020) analysis of COVID-19 in South Korea:

library(EpiAwareR)

# Load data
training_data <- read.csv("https://raw.githubusercontent.com/EpiAware/PrototypeCompositionalProbablisticInfectiousDiseaseModelling/refs/heads/main/data/south_korea_data.csv")
colnames(training_data)[3] <- "cases"

# Define latent process: AR(2) for log Rt
ar2 <- AR(
  order = 2,
  damp_priors = list(
    truncnorm(0.2, 0.2, 0, 1),
    truncnorm(0.1, 0.05, 0, 1)
  ),
  init_priors = list(norm(0, 0.2), norm(0, 0.2)),
  std_prior = halfnorm(0.1)
)

# Define infection model: Renewal process
renewal <- Renewal(
  gen_distribution = gamma_dist(6.5, 0.62),
  initialisation_prior = norm(log(1.0), 0.1)
)

# Define observation model: Negative binomial
negbin <- NegativeBinomialError(
  cluster_factor_prior = halfnorm(0.1)
)

# Compose into complete model
model <- EpiProblem(
  epi_model = renewal,
  latent_model = ar2,
  observation_model = negbin,
  tspan = c(45, 80)
)

# Fit model
results <- fit(
  model,
  data = training_data,
  method = nuts_sampler(warmup = 1000, draws = 1000, chains = 4)
)

# Examine results
print(results)
summary(results)
plot(results, type = "Rt")
plot(results, type = "cases")

Development Status

EpiAwareR is currently in MVP (Minimum Viable Product) phase, implementing core components needed for the Mishra et al. (2020) case study.

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License. See LICENSE for details.

Contributors

All contributions to this project are gratefully acknowledged using the allcontributors package following the allcontributors specification. Contributions of any kind are welcome!

Code

sbfnk

Issues

seabbs, owenjonesuob