{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T12:25:07Z","timestamp":1782995107931,"version":"3.54.5"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":297,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p><jats:italic>Introduction<\/jats:italic>. Dengue, a vector\u2010borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east\u2010coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time\u2010based estimate of possible dengue incidence increase following weather\u2010related changes, thereby highlighting potential dengue outbreaks. <jats:italic>Method<\/jats:italic>. All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6\u00b010\u2009<jats:sup>\u2032<\/jats:sup>N, longitude 102\u00b018\u2009<jats:sup>\u2032<\/jats:sup>E) was analysed with dengue data. <jats:italic>Result<\/jats:italic>. A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. <jats:italic>Conclusion<\/jats:italic>. Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.<\/jats:p>","DOI":"10.1155\/2021\/3540964","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T02:50:08Z","timestamp":1635216608000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1070-5496","authenticated-orcid":false,"given":"Afiqah Syamimi","family":"Masrani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-0838","authenticated-orcid":false,"given":"Nik Rosmawati","family":"Nik Husain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3708-0628","authenticated-orcid":false,"given":"Kamarul Imran","family":"Musa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Syaarani","family":"Yasin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"e_1_2_8_1_2","unstructured":"World Health Organization Dengue and severe dengue 2020 https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dengue-and-severe-dengue."},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2017.00317"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40572-016-0078-z"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.actatropica.2019.105055"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/09603123.2019.1656801"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph10126319"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2016.07.026"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16142486"},{"key":"e_1_2_8_9_2","first-page":"113","article-title":"Spatial pattern of 2009 dengue distribution in Kuala Lumpur using GIS application","volume":"29","author":"Aziz S.","year":"2012","journal-title":"Tropical Biomedicine"},{"key":"e_1_2_8_10_2","first-page":"237","article-title":"Aedes larval population dynamics and risk for dengue epidemics in Malaysia","volume":"28","author":"Rohani A. 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