The COVID-19 pandemic placed tremendous strain on medical resources. To assess intervention strategies and resource planning for metropolitan pandemic response, a hybrid System Dynamics–Discrete-Event Simulation (SD-DES) framework is developed, using Wuhan as a case study. With SD capturing macroscopic infection dynamics and policy intervention and DES simulating medical resource scheduling, bidirectional feedback between macro and micro levels enables a more holistic and comprehensive evaluation of policy effects. By simulating 132 representative scenarios, key factors including containment measures, medical resource capacity, mask adoption, vaccine rollout speed, and the timing of external medical support were examined. Our analysis provides a comprehensive perspective on pandemic prevention and control decision-making, an aspect often underrepresented in prior studies. The model offers an extensible computational framework for complex socioeconomic systems, such as pandemic emergencies, where dynamic and process complexities coexist. It can assist policymakers in enhancing the healthcare system’s preparedness and mitigating the spread of the pandemic. Future research could improve model robustness by incorporating advanced parameter estimation techniques, such as particle filtering. To address computational challenges, we also recommend implementing asynchronous module execution and optimized programming to improve simulation efficiency and scalability.
Zhu et al. (Tue,) studied this question.