Resilience and Criticality of Interdependent Networks in Wuhan During the 2020 COVID-19 Outbreak: A Multilayer Spatiotemporal Review

This study investigates the interdependent dynamics between public transport and healthcare systems during the 2020 COVID-19 outbreak in Wuhan, China. Employing a multilayer spatiotemporal framework, the analysis identifies structural vulnerabilities in metro networks and healthcare infrastructure that contributed to viral transmission. Using predictive passenger flow data, proximity centrality metrics, and an adapted SIR epidemiological model, the research assesses network criticality and resilience under epidemic-induced stress. Empirical findings indicate that high passenger density nodes and proximity to epidemic sites significantly influenced infection propagation. The study also examines cascading failures in transport systems and their effect on hospital accessibility. Results inform an integrated urban planning approach, emphasizing spatial correlation between transit accessibility and healthcare coverage. The analytical framework incorporates CNN-LSTM-based traffic classification to enhance real-time assessment. Conclusions highlight the necessity of reconfiguring urban mobility systems to strengthen epidemiological containment strategies and infrastructure interconnectivity under public health emergencies.