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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Diffusion of sylvatic yellow fever in the state of Sao Paulo, Brazil

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Autor(es):
Lacerda, Alec Brian [1] ; del Castillo Saad, Leila [2] ; Ikefuti, Priscilla Venancio [1] ; Pinter, Adriano [3] ; Chiaravalloti-Neto, Francisco [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Sch Publ Hlth, Dept Epidemiol, Av Dr Arnaldo 715, Sao Paulo, SP - Brazil
[2] Prof Alexandre Vranjac Hlth Secretariat State Sa, Epidemiol Surveillance Ctr, Sao Paulo, SP - Brazil
[3] SUCEN, Endem Control Superintendence, Sao Paulo, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: SCIENTIFIC REPORTS; v. 11, n. 1 AUG 11 2021.
Citações Web of Science: 0
Resumo

We investigated the sylvatic yellow fever (SYF) diffusion process in Sao Paulo (SP) between 2016 and 2019. We developed an ecological study of SYF through autochthonous human cases and epizootics of non-human primates (NHPs) that were spatiotemporally evaluated. We used kriging to obtain maps with isochrones representative of the evolution of the outbreak and characterized its diffusion pattern. We confirmed 648 human cases of SYF in SP, with 230 deaths and 843 NHP epizootics. Two outbreak waves were identified: one from West to East (2016 and 2017), and another from the Campinas region to the municipalities bordering Rio de Janeiro, Minas Gerais, and Parana and those of the SP coast (2017-2019). The SYF outbreak diffusion process was by contagion. The disease did not exhibit jumps between municipalities, indicating that the mosquitoes and NHPs were responsible for transmitting the virus. There were not enough vaccines to meet the population at risk; hence, health authorities used information about the epizootic occurrence in NHPs in forest fragments to identify priority populations for vaccination. (AU)

Processo FAPESP: 20/01596-8 - Uso de sensoriamento remoto e inteligência artificial para prever áreas com alto risco de infestação por Aedes aegypti e arboviroses
Beneficiário:Francisco Chiaravalloti Neto
Modalidade de apoio: Auxílio à Pesquisa - Regular