Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Health Care System Administration...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Conflict Studies

Background:

• Simultaneous large-scale crises like wars and pandemics pose significant societal challenges.
• Understanding epidemic spread during warfare is crucial for effective containment in conflict zones.
• The impact of warfare on epidemic dynamics is an underexplored area.

Purpose of the Study:

• To develop a novel mathematical model integrating epidemiological and war dynamics.
• To explore the dual influence of war and pandemic on population mortality.
• To investigate healthcare administration policies in dual-use systems.

Main Methods:

• Integrated SIR (susceptible-infected-recovered) and Lanchester models.
• Agent-based simulation for in silico data generation.
• Deep reinforcement learning (deep Q-network) for healthcare policy optimization.

Main Results:

• War and pandemic scenarios lead to chaotic dynamics.
• Healthcare systems should prioritize immediate mortality reduction.
• Policy decisions involve choosing between war-injured soldiers and pandemic-infected civilians based on immediate impact.

Conclusions:

• Integrating conflict factors into epidemic models is vital.
• Enhances preparedness and response strategies in conflict-affected areas.
• Highlights the need for dynamic, context-specific healthcare policies during crises.