Machine learning methods to predict epidemiological behavior of arbovirals diseases: structured literature review
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Keywords

Review; Arboviral Infections; Public Health Surveillance; Forecasting; Machine Learning; Bibliometrics

How to Cite

Polo-Triana, S. I., Ramírez-Sierra, Y. A., Arias-Osorio, J. E., Martínez-Vega, R. A., & Lamos-Díaz, H. (2022). Machine learning methods to predict epidemiological behavior of arbovirals diseases: structured literature review . Salud UIS, 55. https://doi.org/10.18273/saluduis.55.e:23017

Abstract

Introduction: Machine learning methods allow to manipulate structured and unstructured data to build predictive models and support decision-making. Objective: To identify machine learning methods applied to predict the epidemiological behavior of vector-borne diseases using epidemiological surveillance data. Methodology: A literature search in EMBASE and PubMed, bibliometric analysis, and information synthesis were performed. Results: A total of 41 papers were selected, all of them were published in the last decade. The most frequent keyword was dengue. Most authors (88.3 %) participated in a research article. Sixteen machine learning methods were found, the most frequent being Artificial Neural Network, followed by Support Vector Machines. Conclusions: In the last decade there has been an increase in the number of articles that aim to predict the epidemiological behavior of vector-borne diseases using by means of various machine learning methods that incorporate time series of cases, climatological variables, and other sources of open data information. 

https://doi.org/10.18273/saluduis.55.e:23017
PDF (Español (España))

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Copyright (c) 2022 Sonia Isabel Polo-Triana, Yuly Andrea Ramírez-Sierra, Javier Eduardo Arias-Osorio, Ruth Aralí Martínez-Vega, Henry Lamos-Díaz

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