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Revista de la Universidad Industrial de Santander. Salud
versão impressa ISSN 0121-0807versão On-line ISSN 2145-8464
Resumo
POLO-TRIANA, Sonia Isabel et al. Machine learning methods to predict epidemiological behavior of arbovirals diseases: structured literature review. Rev. Univ. Ind. Santander. Salud [online]. 2023, vol.55, e63. Epub 01-Dez-2023. ISSN 0121-0807. https://doi.org/10.18273/saluduis.55.e:23017.
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.
Palavras-chave : Review; Arboviral infections; Public health surveillance; Forecasting; Machine learning; Bibliometrics.