Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018.

Journal: Revista da Sociedade Brasileira de Medicina Tropical
Published Date: Jan 1, 2022

Abstract

BACKGROUND: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease.

Authors

  • Matheus Félix Xavier Barboza
    Universidade de Pernambuco, Programa de Pós-Graduação em Engenharia da Computação, Recife, PE, Brasil.
  • Kayo Henrique de Carvalho Monteiro
    Universidade de Pernambuco, Recife 50100-010, Brazil. khcm@ecomp.poli.br.
  • Iago Richard Rodrigues
    Universidade Federal de Pernambuco, Centro de Informática, Recife, PE, Brasil.
  • Guto Leoni Santos
    Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-901, Brazil. guto.leoni@gprt.ufpe.br.
  • Wuelton Marcelo Monteiro
    Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Amazonas 69040-000,Brazil.
  • Elder Augusto Guimaraes Figueira
    Fundação de Vigilância em Saúde Rosemary Costa Pinto, Manaus, AM, Brasil.
  • Vanderson de Souza Sampaio
    Fundação de Medicina Tropical Doutor Heitor Vieira Dourado, Manaus, AM, Brasil.
  • Theo Lynn
    Business School, Dublin City University, Dublin 9, Ireland. theo.lynn@dcu.ie.
  • Patricia Takako Endo
    Programa de Pós-Graduação em Engenharia da Computação Universidade de Pernambuco (UPE) Recife Pernambuco Brazil.

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