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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Evaluating mobility restrictions through spatiotemporal effective reproduction number analysis in a multi-patch model
Byul Nim Kim1, Minchan Choi1, Hyosun Lee1
1Department of Applied Mathematics, Kyung Hee University, Yongin, 17104, Republic of Korea.
Epidemics
|January 7, 2026
Summary
Understanding COVID-19 spread requires analyzing mobility and interventions. Densely connected regions like Seoul and Gyeonggi significantly drive transmission, necessitating adaptive, phase-dependent public health strategies.
Area of Science:
- Computational epidemiology
- Infectious disease dynamics
- Public health modeling
Background:
- COVID-19 transmission is complex, influenced by mobility, connectivity, and interventions, making region-specific risk assessment challenging.
- Effective epidemic preparedness demands understanding spatial and temporal disease spread dynamics.
Purpose of the Study:
- To develop a robust computational framework for assessing interregional COVID-19 transmission dynamics.
- To quantify the impact of mobility and interventions on disease spread across different epidemic phases.
- To identify key transmission hubs and evaluate targeted intervention strategies.
Main Methods:
- Utilized a multi-patch model to estimate time-dependent regional effective reproduction numbers.
- Integrated high-resolution mobility and COVID-19 incidence data from South Korea.
- Distinguished locally transmitted infections from mobility-induced cases.
Main Results:
- Seoul and Gyeonggi were identified as dominant sources of interregional COVID-19 spread, with influence varying by pandemic phase.
- Mobility controls in identified transmission hubs significantly reduced infection spread during the Pre-Delta phase.
- Densely connected regions disproportionately contribute to nationwide transmission.
Conclusions:
- Adaptive, phase-dependent intervention strategies are more effective than uniform nationwide policies for controlling COVID-19.
- Integrating real-world mobility data with epidemic modeling provides a scalable framework for data-driven public health responses.
- Targeted interventions in key transmission hubs are crucial for mitigating widespread infectious disease transmission.
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