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Human mobility and outbreak origins in epidemic spread: Insights from agent-based modeling
Konstantin A Klochkov1,2, Ivan E Kozlov1, Elena N Ilina1
1Research Institute for Systems Biology and Medicine, Moscow, Russia.
Plos Computational Biology
|May 12, 2026
Summary
Human mobility significantly impacts epidemic spread. This study introduces transCovasim to model travel's effect on disease dynamics, revealing how mobility restrictions can mitigate outbreaks and aid in identifying infection origins.
Area of Science:
- Epidemiology
- Computational Biology
- Network Science
Background:
- Human mobility is a primary factor in the rapid dissemination of infectious diseases.
- Non-pharmaceutical interventions, such as travel restrictions, are crucial for controlling epidemic spread.
- Understanding the intricate relationship between mobility patterns and epidemic metrics is essential for effective public health strategies.
Purpose of the Study:
- To develop and utilize transCovasim, an agent-based model, for simulating inter-city disease transmission dynamics.
- To investigate the impact of varying mobility flows on epidemic timing, peak magnitude, and variability.
- To assess the effectiveness of mobility-targeted interventions and explore methods for early outbreak source attribution.
Main Methods:
- Developed transCovasim, an agent-based model extending Covasim, to simulate coupled city dynamics via traveler exchange.
- Analyzed two-city systems with varied population sizes and a hub-and-satellite network under Moscow-like commuting conditions.
- Employed dynamic time warping on surveillance data to evaluate outbreak origin identification accuracy.
Main Results:
- Inter-city travel delays epidemic peaks, with variability decreasing as travel increases.
- Mobility primarily redistributes infections; peak magnitudes are less sensitive to directional flows in comparable cities.
- Reducing commuting flows, especially early on, significantly lowers incidence; late interventions show diminishing returns.
- Outbreak origin identification accuracy reaches 78% by day 50 with 10% testing under Moscow-like conditions.
Conclusions:
- Specific mobility patterns critically influence epidemic timing and disease burden.
- Mobility-targeted non-pharmaceutical interventions offer actionable strategies for epidemic control.
- The transCovasim model provides a robust framework for analyzing disease spread in connected populations and aids in early outbreak investigations.
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