{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:55:14Z","timestamp":1779897314830,"version":"3.53.1"},"reference-count":44,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio\u2010temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio\u2010temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study used district\u2010level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (<jats:italic>\u03b2<\/jats:italic>\u2009=\u20099.56, standard error [SE]: 3.48) and maximum temperature (<jats:italic>\u03b2<\/jats:italic>\u2009=\u20091.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (<jats:italic>\u03b2<\/jats:italic>\u2009=\u20092.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (<jats:italic>\u03b2<\/jats:italic>\u2009=\u20095.57, SE: 2.50) and mumps (<jats:italic>\u03b2<\/jats:italic>\u2009=\u200911.99, SE: 3.13) diseases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high\u2010risk districts for airborne diseases in the country.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hsr2.2176","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T12:30:25Z","timestamp":1718800225000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatio\u2010temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1498-287X","authenticated-orcid":false,"given":"Arman Hossain","family":"Chowdhury","sequence":"first","affiliation":[{"name":"Department of Statistics Begum Rokeya University, Rangpur  Rangpur Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8925-6544","authenticated-orcid":false,"given":"Md. 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