Static–Dynamic Analytical Framework for Urban Health Resilience Evaluation and Influencing Factor Exploration from the Perspective of Public Health Emergencies—Case Study of 61 Cities in Mainland China

Le résumé fourni par la source

With the acceleration of urbanization, citizens are facing more pandemic challenges. A deeper understanding of constructing more resilient cities can help citizens be better prepared for potential future pandemics or disasters. In this study, a static–dynamic analytical framework for urban health resilience evaluation and influencing factor exploration was proposed, which helped not only to analyze the basic static urban health resilience (SUHRI) under normal conditions but also to evaluate the dynamic urban health resilience (DURHI) under an external epidemic shock. The epidemic dynamic model could reasonably simulate the epidemic change trend and quantitatively evaluate resistance and recovery capacity, and the proposed influencing factor exploration model improved the model fitness by filtering out the influence of population flow autocorrelation existing in the residuals. SUHRI and DUHRI, and their corresponding key influencing factors, were compared and discussed. The results of the static–dynamic integration and difference score showed that 60.6% cities within the study area had a similar performance on SUHRI and DUHRI, but there was also a typical difference: some regional hubs exhibited high SUHRI but had only mid-level DUHRI, which was attributed to stronger external disturbances such as higher population mobility. The key influencing factors for static and dynamic urban health resilience also vary. Hospital capacity and income had the strongest influence on static urban health resilience but a relatively weaker or even non-significant correlation with dynamic urban health resilience sub-indices. The extracted population flow eigenvector collection had the strongest influence on dynamic urban health resilience, as it represents the population flow connection among cities, which could reflect the information of policy response, such as policy stringency and support intensity. We hope that our study will shed some light on constructing more resilient urban systems and being prepared for future public health emergencies.

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