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  3. E3sim: Epidemiological-ecological-evolutionary Simulation Framework For Genomic Epidemiology.
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  3. E3sim: Epidemiological-ecological-evolutionary Simulation Framework For Genomic Epidemiology.

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e3SIM: Epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

Peiyu Xu1, Shenni Liang2,3, Andrew Hahn2

  • 1Department of Molecular Biology & Genetics, Cornell University, Ithaca, New York, USA.

Methods in Ecology and Evolution
|April 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

e3SIM is a new simulator that models infectious disease spread by integrating epidemiological, ecological, and evolutionary processes. This tool enhances the realism and predictive accuracy of genomic epidemiology for public health.

Keywords:
agent-based simulationepi-eco-evo couplinggenetic epidemiologyphylodynamicspopulation genetics

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Area of Science:

  • Epidemiology
  • Evolutionary Biology
  • Computational Biology

Background:

  • Infectious disease dynamics involve complex epidemiological, ecological, and evolutionary (epi-eco-evo) interactions.
  • Current genomic epidemiology simulators often simplify these processes, assuming independence between transmission and pathogen evolution, limiting realistic modeling.
  • This simplification fails to capture how pathogen evolution dynamically influences epidemic trajectories.

Purpose of the Study:

  • To introduce e3SIM, an agent-based simulator that explicitly integrates epi-eco-evo processes for more realistic infectious disease modeling.
  • To provide a flexible and user-friendly tool for exploring pathogen spread and evolution under various ecological and epidemiological conditions.
  • To enhance the predictive accuracy of genomic epidemiology by accounting for coupled disease dynamics.

Main Methods:

  • Developed e3SIM, an open-source, agent-based, forward-time simulator integrating transmission, molecular evolution, and environmental factors.
  • Incorporated configurable compartmental models, host contact networks, pathogen genetic architectures, and eco-evolutionary features (e.g., within-host dynamics, multi-strain infections).
  • Demonstrated capabilities through simulations of SARS-CoV-2 and Mycobacterium tuberculosis outbreaks, including drug-resistant variant emergence and superspreader effects.

Main Results:

  • e3SIM accurately captured the emergence and spread of drug-resistant variants under simulated sequential treatments.
  • The simulator highlighted how pathogen evolution and environmental variations dynamically reshape epidemic trajectories.
  • Simulations showed that pathogen transmissibility and host population structures, including superspreaders, significantly influence lineage expansion and transmission clusters.

Conclusions:

  • e3SIM offers a powerful, integrated approach to simulating infectious disease dynamics, enhancing realism and predictive accuracy in genomic epidemiology.
  • The simulator's modular and user-friendly design supports diverse host-pathogen systems and critical public health scenario exploration.
  • Explicitly integrating epi-eco-evo processes in e3SIM advances our understanding of pathogen spread and evolution, informing public health strategies.