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

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.