{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:38:51Z","timestamp":1770748731539,"version":"3.50.0"},"reference-count":80,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Risk factors at different stages of COVID\u201019 may interact with each other, forming a risk network. Identifying the key risk factors within this network and their interrelationships is crucial for reducing the overall risk of COVID\u201019. We constructed three Bayesian Belief Network (BBN) models by combining data\u2010driven approaches with expert validation. Using the Tree\u2010Augmented Naive Bayes (TAN) algorithm, we developed the INFORM COVID\u201019 Risk BBN model and the COVID\u201019 Regional Safety Assessment BBN model. The joint BBN model was established using the Greedy Thick Thinning (GTT) algorithm. Parameter learning was performed through maximum likelihood estimation. Expert validation, 10\u2010fold cross\u2010validation, and model performance metrics were employed to comprehensively assess the overall performance of the models. Additionally, mutual information analysis and sensitivity analysis were used to explore the importance of risk factors at each stage and their interdependencies. \u201cINFORM Vulnerability\u201d and \u201cINFORM Lack of Coping Capacity\u201d were identified as the two key risk factors influencing the risk of early outbreak. In the mid\u2010to\u2010late stages of the pandemic, \u201cEmergency Preparedness\u201d and \u201cMonitoring and Detection\u201d had the greatest impact on regional safety and control measures. Furthermore, the joint BBN model indicated that the most important risk factors affecting the overall COVID\u201019 risk were \u201cLack of Coping Capacity,\u201d \u201cGovernment Risk Management Efficiency,\u201d and \u201cRegional Resiliency,\u201d while the influence of other variables was relatively minor. The main contribution of this study lies in identifying the key risk factors at different stages of the pandemic and their interdependencies, providing policymakers with valuable insights for the rational allocation of limited health resources and the formulation of appropriate and effective prevention and control policies.<\/jats:p>","DOI":"10.1111\/risa.70174","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T10:20:42Z","timestamp":1770632442000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Key Factors in Global Public Health Safety Assessment Based on Bayesian Belief Networks During the COVID\u201019 Pandemic"],"prefix":"10.1111","volume":"46","author":[{"given":"Fangyu","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanze","family":"Du","sequence":"additional","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7290-3434","authenticated-orcid":false,"given":"Chunping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health Shandong Second Medical University  Weifang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Wu","sequence":"additional","affiliation":[{"name":"Health Emergency Center Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine)  Beijing China"},{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Chinese Center for Disease Control and Prevention (Chinese Academy of Preventive Medicine) Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/systems12050167"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/21582440211071101"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6142981"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10479\u2010023\u201005437\u20109"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12992\u2010021\u201000677\u20105"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/systems9030068"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.04.016"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.07.026"},{"key":"e_1_2_12_10_1","unstructured":"Deep Knowledge Group.2020a.COVID\u201019 Regional Safety Assessment. 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