{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T12:51:23Z","timestamp":1791982283257,"version":"4.4.0"},"reference-count":36,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"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":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Malaria remains a major global health concern, causing substantial morbidity and mortality, especially in tropical and subtropical regions with limited healthcare resources. Climatic conditions and extreme weather events are increasingly recognized as critical determinants of malaria transmission, yet their relative contributions remain poorly quantified. This study applied machine learning (ML) and explainable AI (XAI) to identify the key climatic and extreme weather determinants of malaria, assess their relative importance, and predict incidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Malaria incidence was analyzed across 78 endemic countries (2000\u20132022) using climatic and extreme weather variables from the World Health Organization (WHO), the EM\u2010DAT International Disaster Database, and the Global Data Lab. Random forest (RF) model was trained to predict malaria incidence, with performance assessed using the coefficient of determination (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ), root mean square error (RMSE), and mean absolute error (MAE). To enhance interpretability and identify influential predictors, explainable AI (XAI) was applied using SHapley Additive exPlanations (SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Malaria incidence declined steadily across the 78 endemic countries over the study period, remaining highest in Sub\u2010Saharan Africa. Random forest achieved the best overall predictive performance for malaria incidence (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.88, 95% CI: 0.84\u20130.92; RMSE\u2009=\u200939.21, 95% CI: 35.29\u201343.13; MAE\u2009=\u200927.33, 95% CI: 23.41\u201331.25) with gradient boosting performing comparably. SHAP analysis identified the leading predictors associated with malaria incidence as average annual surface temperature and average annual precipitation, followed by storm\u2010 and flood\u2010affected population metrics. Climatic variables contributed substantially more to model predictions than disaster\u2010related indicators.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Random forest integrated with explainable AI provides an effective and interpretable framework for predicting malaria incidence and identifying its key climatic predictors, while quantifying the secondary contribution of extreme weather exposure. These findings can support evidence\u2010based, climate\u2010adaptive public health planning for malaria prevention and control.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.73311","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T06:31:00Z","timestamp":1790663460000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Machine Learning for Predicting Global Malaria Incidence From 2000 to 2022 With Climatic and Extreme Weather Conditions Across 78 Endemic Countries: A Retrospective Ecological Study"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0424-446X","authenticated-orcid":false,"given":"Md. Abu Bokkor","family":"Shiddik","sequence":"first","affiliation":[{"name":"Department of Statistics Begum Rokeya University Rangpur Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3098-653X","authenticated-orcid":false,"given":"Md. Abdullah Al Fahad","family":"Sohan","sequence":"additional","affiliation":[{"name":"Department of Statistics Begum Rokeya University Rangpur Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"e_1_2_12_2_1","volume-title":"World Health Organization","year":"2025"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)31097-9"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-12872-0"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/ele.13335"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-10015-z"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12936-025-05431-5"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/infdis\/jiw363"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.lana.2025.101189"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12936-026-05847-7"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00436-024-08276-0"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgph.0006104"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature15535"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-50176-3"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0299386"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"e_1_2_12_17_1","unstructured":"\u201cIndicators \u201d accessed June 18 2026 https:\/\/www.who.int\/data\/gho\/data\/indicators."},{"key":"e_1_2_12_18_1","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED) \u201cEM\u2010DAT \u201d published 2024 https:\/\/public.emdat.be\/."},{"key":"e_1_2_12_19_1","unstructured":"J.SmitsandI.Permanyer \u201cGlobal Data Lab\u201d (Nijmegen: Radboud University 2024) https:\/\/globaldatalab.org\/."},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.3329\/ijss.v25i2.85772"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.17148\/IARJSET.2015.2305"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.4236\/ojs.2015.57075"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jesp.2017.09.011"},{"key":"e_1_2_12_24_1","unstructured":"Python Software Foundation \u201cPython 3.13.0. 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