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. 2022 Jun 3:10:900077.
doi: 10.3389/fpubh.2022.900077. eCollection 2022.

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

Clarisse Lins de Lima  1 , Ana Clara Gomes da Silva  1 , Giselle Machado Magalhães Moreno  2 , Cecilia Cordeiro da Silva  3 , Anwar Musah  4 , Aisha Aldosery  4 , Livia Dutra  2 , Tercio Ambrizzi  2 , Iuri V G Borges  2 , Merve Tunali  5 , Selma Basibuyuk  5 , Orhan Yenigün  5 , Tiago Lima Massoni  6 , Ella Browning  7 , Kate Jones  7 , Luiza Campos  8 , Patty Kostkova  4 , Abel Guilhermino da Silva Filho  3 , Wellington Pinheiro Dos Santos  9
Affiliations

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

Clarisse Lins de Lima et al. Front Public Health. .

Abstract

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.

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

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
This system consisted of the following steps: (1) First, we performed a search of scientific databases (IEEE Xplore, PubMed, Scopus, Science Direct, and Springer Link). (2) We then filtered the returned articles according to the exclusion criteria. (3) In the next step, we selected the article that remained from the previous stage according to the inclusion criteria. (4) After completing the previous step, we read, evaluated, and summarized the studies included in the review. (5) In the last step of this review, we grouped the studies considering their common characteristics.
Figure 2
Figure 2
Distribution of the number of articles according to the year of publication for each group.
Figure 3
Figure 3
Average score for each quality criteria for the studies from each group.
See this image and copyright information in PMC

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