epiworldR: Fast Agent-Based Epi Models

Journal of Open Source Software Β· 2023

Agent-based modeling (ABM) has emerged as a powerful computational approach to studying complex systems across various fields, including social sciences and epidemiology.

Published2023Cited by2

Citation counts from OpenAlex; downloads from CRAN. Updated 2026-08-15.

  1. Meyer, D.University of Utah Data Coordinating Center
  2. Vega Yon, G. G.University of UtahiD

DOIβ€’Publisher / Preprint

Abstract

Agent-based modeling (ABM) has emerged as a powerful computational approach to studying complex systems across various fields, including social sciences and epidemiology. By simulating the interactions and behaviors of individual entities, known as agents, ABM provides a unique lens through which researchers can analyze and understand the emergent properties and dynamics of these systems. The epiworldR package provides a flexible framework for ABM implementation and methods for prototyping disease outbreaks and transmission models using a C++ backend. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify transmission/susceptibility rates as a function of agents’ features, providing great complexity for the model dynamics.

Cite

@article{meyerEpiworldRFastAgentBased2023,
  title = {epiworldR: Fast Agent-Based Epi Models},
  author = {{Meyer}, {Derek} and {Vega Yon}, {George G.}},
  year = {2023},
  month = {oct},
  journal = {Journal of Open Source Software},
  volume = {8},
  number = {90},
  pages = {5781},
  doi = {10.21105/joss.05781},
  url = {https://doi.org/10.21105/joss.05781},
  issn = {2475-9066},
}

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