ORIGINAL RESEARCH article
Front. Public Health
Sec. Disaster and Emergency Medicine
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1691666
This article is part of the Research TopicRethinking Community Resilience: Cultural Approaches to Social Cohesion and Social Capital in Crisis ContextsView all articles
Measuring and Optimizing the Urban Community Resilience against Public Health Emergencies: A case study in Nanjing China
Provisionally accepted
- 1Nanjing Forestry University, Nanjing, China
- 2Umea Universitet, Umeå, Sweden
- 3University of Maryland, College Park, United States
- 4The Hong Kong Polytechnic University, Hong Kong, Hong Kong, SAR China
- 5Southeast University, Nanjing, China
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With the acceleration of urbanization, communities, as the basic unit of urban governance, play a crucial role in responding to public health emergencies (PHEs). This study aims to investigate the resilience measurement and optimization strategies of urban communities in responding to PHEs in order to improve their resilience. The study was based on constructing a resilience assessment framework and identifying 31 key influencing factors to measure the resilience of case communities in Nanjing. Through sensitivity analysis, this paper proposes a static optimization strategy for urban community resilience from three levels: social, environmental and economic. Through dynamic Bayesian network inference simulation and importance analysis, the dynamic optimization strategy of urban community public health prevention and control resilience is proposed from the perspective of pre, medium and long-term. Through the combination of dynamic and static strategies, community managers promote resilience building from both short-term and long-term perspectives, which provides a valuable reference for comprehensively improving the emergency management system.
Keywords: Urban community resilience, Public health emergencies, Bayesian network, Resilience metrics, Optimization strategies
Received: 24 Aug 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Cui, Saiya, Qin, Zhang, Li and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Peng Cui, cui.peng@umu.se
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