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A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197220/ https://www.ncbi.nlm.nih.gov/pubmed/35712272 http://dx.doi.org/10.3389/fpubh.2022.884645 |
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author | Bhatia, Surbhi Bansal, Dhruvisha Patil, Seema Pandya, Sharnil Ilyas, Qazi Mudassar Imran, Sajida |
author_facet | Bhatia, Surbhi Bansal, Dhruvisha Patil, Seema Pandya, Sharnil Ilyas, Qazi Mudassar Imran, Sajida |
author_sort | Bhatia, Surbhi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9197220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91972202022-06-15 A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions Bhatia, Surbhi Bansal, Dhruvisha Patil, Seema Pandya, Sharnil Ilyas, Qazi Mudassar Imran, Sajida Front Public Health Public Health 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. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9197220/ /pubmed/35712272 http://dx.doi.org/10.3389/fpubh.2022.884645 Text en Copyright © 2022 Bhatia, Bansal, Patil, Pandya, Ilyas and Imran. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Bhatia, Surbhi Bansal, Dhruvisha Patil, Seema Pandya, Sharnil Ilyas, Qazi Mudassar Imran, Sajida A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions |
title | A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions |
title_full | A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions |
title_fullStr | A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions |
title_full_unstemmed | A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions |
title_short | A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions |
title_sort | retrospective study of climate change affecting dengue: evidences, challenges and future directions |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197220/ https://www.ncbi.nlm.nih.gov/pubmed/35712272 http://dx.doi.org/10.3389/fpubh.2022.884645 |
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