{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T10:44:54Z","timestamp":1791456294575,"version":"4.3.4"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.<\/jats:p>","DOI":"10.3389\/fpubh.2022.884645","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T08:22:28Z","timestamp":1654071748000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions"],"prefix":"10.3389","volume":"10","author":[{"given":"Surbhi","family":"Bhatia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhruvisha","family":"Bansal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seema","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharnil","family":"Pandya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qazi Mudassar","family":"Ilyas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajida","family":"Imran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00848-6","article-title":"Systematic mapping study of AI\/machine learning in healthcare and future directions","volume":"2","author":"Parashar","year":"2021","journal-title":"SN Comput Sci"},{"key":"B2","doi-asserted-by":"publisher","first-page":"192","DOI":"10.5465\/amr.2018.0072","article-title":"Artificial intelligence and management: the automation-augmentation paradox","volume":"46","author":"Raisch","year":"2021","journal-title":"Acad Manag Rev"},{"key":"B3","doi-asserted-by":"publisher","first-page":"101741","DOI":"10.1016\/j.techsoc.2021.101741","article-title":"Artificial intelligence, systemic risks, and sustainability","volume":"67","author":"Galaz","year":"2021","journal-title":"Technol Soc"},{"key":"B4","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-030-67051-1_15","article-title":"A review of machine learning approaches in clinical healthcare","volume":"113","author":"Dixit","year":"2021","journal-title":"Intell Healthcare"},{"key":"B5","doi-asserted-by":"publisher","first-page":"S67","DOI":"10.1016\/j.gaceta.2020.12.019","article-title":"Artificial intelligence in healthcare: opportunities and risk for future","volume":"35","author":"Sunarti","year":"2021","journal-title":"Gaceta Sanitaria"},{"key":"B6","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/s00146-020-01085-w","article-title":"Artificial intelligence in medicine and the disclosure of risks","volume":"36","author":"Kiener","year":"2021","journal-title":"Ai & Society"},{"key":"B7","author":"Hannah","year":"2021","journal-title":"Climate Change Biology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/0952813X.2022.2058097","article-title":"COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients","author":"Iwendi","year":"2022","journal-title":"J Exp Theor Artif Intell"},{"key":"B9","unstructured":"What Is Climate Change? 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