Dengue prediction in Latin America using machine learning and the one health perspective: a literature review

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dc.date.accessioned2023-04-05T12:47:58Z
dc.date.available2023-04-05T12:47:58Z
dc.date.issued2022
dc.description.abstractDengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever. © 2022 by the authors.
dc.identifier.issn24146366
dc.identifier.urihttps://doi.org/10.3390/tropicalmed7100322
dc.identifier.urihttp://146.190.124.33/handle/123456789/6098
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.sourceRevista de Investigaciones Veterinarias del Peru; Vol. 31 Núm. 2
dc.titleDengue prediction in Latin America using machine learning and the one health perspective: a literature reviewes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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