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

Journal: Frontiers in public health
PMID: 35719644

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.

Authors

  • Clarisse Lins de Lima
    Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil.
  • Ana Clara Gomes da Silva
    Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil.
  • Giselle Machado Magalhães Moreno
    Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil.
  • Cecilia Cordeiro da Silva
    Center for Informatics, Federal University of Pernambuco, CIn-UFPE, Recife, Brazil.
  • Anwar Musah
    Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom.
  • Aisha Aldosery
    Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom.
  • Livia Dutra
    Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil.
  • Tercio Ambrizzi
    Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil.
  • Iuri V G Borges
    Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil.
  • Merve Tunali
    Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey.
  • Selma Basibuyuk
    Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey.
  • Orhan Yenigün
    Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey.
  • Tiago Lima Massoni
    Department of Systems and Computing, Federal University of Campina Grande, Campina Grande, Brazil.
  • Ella Browning
    Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.
  • Kate Jones
    Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.
  • Luiza Campos
    Department of Civil Environmental and Geomatic Engineering, University College London, London, United Kingdom.
  • Patty Kostkova
    University College London (UCL) Center for Digital Public Health in Emergencies (dPHE), Institute for Risk and Disaster Reduction, University College London, London, United Kingdom.
  • Abel Guilhermino da Silva Filho
    Center for Informatics, Federal University of Pernambuco, CIn-UFPE, Recife, Brazil.
  • Wellington Pinheiro Dos Santos
    Department of Biomedical Engineering, Federal University of Pernambuco, DEBM-UFPE, Recife, Brazil.

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