Evidence map›Paper›PMID 39991698›Full record

ArticleFrontiers in public health2025

Scenario construction and evolutionary analysis of nonconventional public health emergencies based on Bayesian networks.

Yutao Zhu, Qing Yang, Lingmei Fu, Chun Cai, Jinmei Wang, Ling He

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Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Yutao ZhuSchool of Management, Wuhan University of Technology, Wuhan, China.
Qing YangSchool of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China.
Lingmei FuCollege of Emergency Management, Nanjing Tech University, Nanjing, China.
Chun CaiSchool of Automotive Engineering, Wuhan University of Technology, Wuhan, China.
Jinmei WangSchool of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China.
Ling HeSchool of Management, Wuhan University of Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: 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. Study design: 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. Methods: 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. Results: 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. Conclusion: 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.

Indexed as

COVID-19EmergenciesPublic HealthBayes TheoremHumansSARS-CoV-2Bayesian networksCOVID-19emergency responsescenario evolutionunconventional public health emergencies

Identifiers

PMID39991698
PMCPMC11842347

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.