Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

Maritza Cabrera, Jason Leake, José Naranjo-Torres, Nereida Valero, Julio C. Cabrera, Alfonso J. Rodríguez-Morales

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Dengue 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.

Original languageEnglish
Article number322
JournalTropical Medicine and Infectious Disease
Volume7
Issue number10
DOIs
StatePublished - Oct 2022
Externally publishedYes

Keywords

  • climate change
  • dengue
  • epidemiology
  • Latin America
  • machine learning
  • one health
  • prediction

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