{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:12:36Z","timestamp":1789737156310,"version":"4.0.1"},"reference-count":14,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":["Health Science Reports"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Natural disasters, particularly floods, significantly increase infectious disease risk through environmental contamination and healthcare system disruption. Despite well\u2010documented flood\u2010disease associations, predictive models for post\u2010disaster epidemiological surveillance remain limited. We aimed to develop and validate machine learning algorithms to predict infectious disease incidence following flood events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a retrospective pre\u2013post cohort study using routinely collected electronic health records from Firuzkuh County health centers, comparing a 30\u2010day pre\u2010flood cohort (July\u2013August 2021;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009461) with a 30\u2010day post\u2010flood cohort (July\u2013August 2022;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009478). Five classifiers (Random Forest, Logistic Regression, linear SVM, Gradient Boosting, and ANN) were trained and evaluated on a held\u2010out test set using AUC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Post\u2010flood infectious disease prevalence increased significantly from 39.5% to 47.3% (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), with an odds ratio of 1.38 (95% CI: 1.09\u20131.75) and attributable risk of 7.8 percentage points. Among machine learning models, Random Forest achieved the highest predictive performance (AUC\u2009=\u20090.76), followed by Gradient Boosting (0.74), Artificial Neural Network (0.72), Support Vector Machine (0.71), and Logistic Regression (0.69). Age and visit date emerged as the most important predictive features across all models. Unexpectedly, younger patients (mean age 51.0 years) showed higher post\u2010flood infectious disease rates compared to older patients (mean age 57.9 years) in the pre\u2010flood period.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning models demonstrated moderate predictive performance for post\u2010flood infectious disease occurrence. While results show feasibility for AI\u2010based disaster epidemiology, the modest performance indicates that incorporating additional environmental and socioeconomic variables is essential for developing clinically actionable prediction systems for public health emergency response.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.71470","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T07:23:15Z","timestamp":1762500195000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Infectious Disease Incidence After Flooding Using Artificial Intelligence Models: A Retrospective Pre\u2013Post Cohort Study Using Routinely Collected EHR Data"],"prefix":"10.1002","volume":"8","author":[{"given":"Mehdi","family":"Safari","sequence":"first","affiliation":[{"name":"Department of Public Health, School of Public Health &amp; Environmental and Occupational Hazards Control Research Center Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Zali","sequence":"additional","affiliation":[{"name":"Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Hatami","sequence":"additional","affiliation":[{"name":"Department of Public Health, School of Public Health &amp; Environmental and Occupational Hazards Control Research Center Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elnaz Amanzadeh","family":"Jajin","sequence":"additional","affiliation":[{"name":"Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0484-9098","authenticated-orcid":false,"given":"Meisam","family":"Akhlaghdoust","sequence":"additional","affiliation":[{"name":"Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence Shahid Beheshti University of Medical Sciences Tehran Iran"},{"name":"USERN Office, Functional Neurosurgery Research Center Shahid Beheshti University of Medical Sciences Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.glt.2023.09.001"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pdisas.2024.100334"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-85146-0"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-03687-w"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.2196\/44238"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurpub\/ckz111"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2024.2362869"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1215929"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jadr.2023.100704"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.parepi.2020.e00144"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.29355"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/bju.14640"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1542\/pir.2018-0208"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/make5010013"}],"container-title":["Health Science Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hsr2.71470","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/hsr2.71470","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hsr2.71470","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:11:09Z","timestamp":1787245869000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hsr2.71470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":14,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/hsr2.71470"],"URL":"https:\/\/doi.org\/10.1002\/hsr2.71470","archive":["Portico"],"relation":{},"ISSN":["2398-8835","2398-8835"],"issn-type":[{"value":"2398-8835","type":"print"},{"value":"2398-8835","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"2024-11-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e71470"}}