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Updated: Dec 7, 2025

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nosoi: A stochastic agent-based transmission chain simulation framework in r.

Sebastian Lequime1,2, Paul Bastide1,3, Simon Dellicour1,4

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Methods in Ecology and Evolution
|September 28, 2020
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Summary

Reconstructing infectious disease transmission chains is difficult. The new nosoi R package simulates these transmission chains, aiding in the validation of epidemiological models and offering a flexible tool for research and education.

Keywords:
agent‐based simulationinfectious diseasepathogenr packagesimulatorstochastic modeltransmission chain

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Infectious Disease Modeling

Background:

  • Reconstructing transmission chains of infectious agents is crucial but challenging.
  • Existing inference frameworks often lack formal assessment on observed transmission data.
  • There is a need for robust simulation tools to validate epidemiological methods.

Purpose of the Study:

  • To introduce nosoi, an open-source R package for simulating transmission chains.
  • To provide a tunable and expandable agent-based framework for diverse epidemiological scenarios.
  • To offer a tool for validating transmission reconstruction methods and exploring epidemic dynamics.

Main Methods:

  • Developed nosoi, an agent-based modeling framework in R.
  • Implemented user-specified rules for host events like movement and transmission.
  • Designed for single-host and dual-host epidemic simulations.
  • Ensured accessibility via GitHub and CRAN with extensive documentation.

Main Results:

  • nosoi enables the simulation of a wide range of epidemic scenarios.
  • The package facilitates the validation of epidemic modeling and phylodynamic analyses.
  • It provides a framework for exploring the impact of parameter variations on epidemic potential.
  • The intuitive algorithmic approach allows for easy model modification.

Conclusions:

  • nosoi offers a comprehensive and flexible platform for simulating infectious disease transmission.
  • The package serves as a valuable tool for researchers validating reconstruction methods.
  • nosoi is also a practical educational resource for teaching epidemiological dynamics.