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Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Health Care System Administration using Deep Reinforcement Learning

Published online by Cambridge University Press:  21 July 2025

Adi Shuchami  and
Teddy Lazebnik Open the ORCID record for Teddy Lazebnik [Opens in a new window]
Adi Shuchami
Affiliation:
Department of Mathematics, https://ror.org/03nz8qe97 Ariel University , Ariel, Israel
Teddy Lazebnik*
Affiliation:
Department of Mathematics, https://ror.org/03nz8qe97 Ariel University , Ariel, Israel Department of Cancer Biology, https://ror.org/02jx3x895 Cancer Institute, University College London , London, UK
*
Corresponding author: Teddy Lazebnik; Email: lazebnik.teddy@gmail.com
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Abstract

Objectives

Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding the dynamics of epidemic spread during warfare is essential for developing effective containment strategies in complex conflict zones. While research has explored epidemic models in various settings, the impact of warfare on epidemic dynamics remains underexplored.

Methods

We proposed a novel mathematical model that integrates the epidemiological SIR (susceptible-infected-recovered) model with the war dynamics Lanchester model to explore the dual influence of war and pandemic on a population’s mortality. Moreover, we consider a dual-use military and civil health care system that aims to reduce the overall mortality rate, which can use different administration policies such as prioritizing soldiers over civilians. Using an agent-based simulation to generate in silico data, we trained a deep reinforcement learning model based on the deep Q-network algorithm for health care administration policy and conducted an intensive investigation on its performance.

Results

Our results show that a pandemic during war conduces chaotic dynamics where the health care system should either prioritize war-injured soldiers or pandemic-infected civilians based on the immediate amount of mortality from each option, ignoring long-term objectives.

Conclusions

Our findings highlight the importance of integrating conflict-related factors into epidemic modeling to enhance preparedness and response strategies in conflict-affected areas.

Keywords

agent-based simulationreinforcement learningresource allocation taskspatio-temporal SIR modelLanchester model

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 19 , 2025 , e197
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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