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Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

期刊

FRONTIERS IN PUBLIC HEALTH
卷 10, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fpubh.2022.900077

关键词

digital epidemiology; computational intelligence; arboviruses forecast; machine learning; systematic review; dengue; chikungunya; Zika virus

资金

  1. FACEPE
  2. CAPES
  3. CNPq [NE/T013664/1]

向作者/读者索取更多资源

This study conducts a systematic literature review to identify arboviruses prediction models and their transmitter vector dynamics. It reveals the challenges in constructing arboviruses prediction models and the existing gap in spatiotemporal models.
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

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