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ORIGINAL RESEARCH article
Front. Public Health
Sec. Disaster and Emergency Medicine
Volume 13 - 2025 |
doi: 10.3389/fpubh.2025.1489904
This article is part of the Research Topic Innovative Strategies for Urban Public Health Resilience in Crisis Situations View all articles
Scenario Construction and Evolutionary Analysis of Nonconventional Public Health Emergencies Based on Bayesian Networks
Provisionally accepted- 1 School of Management, Wuhan University of Technology, Wuhan, China
- 2 School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China
- 3 College of Emergency Management, Nanjing Tech University, Nanjing, Jiangsu Province, China
- 4 School of Automotive Engineering, Wuhan University of Technology, Wuhan, China
- 5 School of Management, Wuhan Institute of Technology, Wuhan, Hubei Province, China
The objective was to aggregate the various scenarios that occur during nonconventional public health emergencies (NCPHEs) and analyze the evolutionary patterns of NCPHEs to better avoid risks and reduce social impacts. The aim was to enhance strategies for handling NCPHEs.News reports were crawled to obtain the scenario elements of NCPHEs and categorized into the spreading stage or derivation stage. Finally, the key scenario nodes and scenario evolution process were analyzed in combination with a corresponding emergency response assessment of each scenario by experts.Dempster-Shafer (DS) theory and Bayesian networks (BNs) were applied for data reasoning, and a spread-derived coupled scenario-response theoretical model of NCPHEs for major public health emergencies was constructed. The scenario evolution path of COVID-19 was derived by combining seven types of major scenario states and corresponding emergency response measures extracted from 952 spreading scenarios.The 26 NCPHE spread scenarios and 41 NCPHE derivation scenarios were summarized.Optimized and pessimistic NCPHE scenario pathways were generated by combining the seven major spreading scenarios to help decision makers predict the development of NCPHEs and take timely and effective emergency response measures for key scenario nodes.This study provides a new approach for understanding and managing NCPHEs, emphasizing the need to consider the specificity and complexity of such emergencies when developing decision-making strategies. Our contextual derivation model and emergency decision-making system provide practical tools with which to enhance NCPHE response capabilities and promote public health and safety.
Keywords: unconventional public health emergencies, Scenario evolution, bayesian networks, Emergency response, COVID-19
Received: 02 Sep 2024; Accepted: 22 Jan 2025.
Copyright: © 2025 Zhu, Yang, Fu, Cai, Wang and He. 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:
Qing Yang, School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China
chun Cai, School of Automotive Engineering, Wuhan University of Technology, Wuhan, China
Jinmei Wang, School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China
Ling He, School of Management, Wuhan Institute of Technology, Wuhan, 430073, Hubei Province, China
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