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

• Published in: Frontiers in public health 2022-06, Vol.10
• Main Authors: Lima, Clarisse Lins de, da Silva, Ana Clara Gomes, Moreno, Giselle Machado Magalhães, Cordeiro da Silva, Cecilia, Musah, Anwar, Aldosery, Aisha, Dutra, Livia, Ambrizzi, Tercio, Borges, Iuri V. G., Tunali, Merve, Basibuyuk, Selma, Yenigün, Orhan, Massoni, Tiago Lima, Browning, Ella, Jones, Kate, Campos, Luiza, Kostkova, Patty, Silva Filho, Abel Guilhermino da, dos Santos, Wellington Pinheiro
• Format: Article
• Language: English
• Subjects: arboviruses forecast; computational intelligence; dengue; digital epidemiology; machine learning; Public Health; systematic review
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2022.900077

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.