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Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.
Martineau, Patrick; Behera, Swadhin K; Nonaka, Masami; Jayanthi, Ratnam; Ikeda, Takayoshi; Minakawa, Noboru; Kruger, Philip; Mabunda, Qavanisi E.
Afiliação
  • Martineau P; Application Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
  • Behera SK; Application Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
  • Nonaka M; Application Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
  • Jayanthi R; Application Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.
  • Ikeda T; Division of Natural Science Solutions, Blue Earth Security Co., Ltd., Tokyo, Japan.
  • Minakawa N; Department of Vector Ecology and Environment, Nagasaki University, Institute of Tropical Medicine, Nagasaki, Japan.
  • Kruger P; Malaria Control Programme, Limpopo Department of Health, Tzaneen, South Africa.
  • Mabunda QE; Malaria Control Programme, Limpopo Department of Health, Tzaneen, South Africa.
Front Public Health ; 10: 962377, 2022.
Article em En | MEDLINE | ID: mdl-36091554

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Clima / Malária Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Africa Idioma: En Revista: Front Public Health Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Clima / Malária Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Africa Idioma: En Revista: Front Public Health Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão