Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study.

Wittwer, Salome; Paolotti, Daniela; Lichand, Guilherme; Leal Neto, Onicio

Wittwer, Salome; Paolotti, Daniela; Lichand, Guilherme; Leal Neto, Onicio.

Afiliação

Wittwer S; Department of Economics, University of Zurich, Zurich, Switzerland.
Paolotti D; Data Science for Social Impact and Sustainability, ISI Foundation, Turin, Italy.
Lichand G; Department of Economics, University of Zurich, Zurich, Switzerland.
Leal Neto O; Institute for Information Security, Department of Computer Science, ETH Zürich, Zurich, Switzerland.

JMIR Public Health Surveill ; 9: e44517, 2023 04 26.

Article em En | MEDLINE | ID: mdl-36888908

Assuntos

COVID-19; Vigilância da População; Brasil/epidemiologia; Autorrelato; COVID-19/epidemiologia; SARS-CoV-2; Pandemias

Palavras-chave

COVID-19; SARS-CoV-2; coronavirus; digital epidemiology; epidemic; forecast; infectious disease; pandemic; participatory surveillance; reporting; self-report; surveillance; trend

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Vigilância da População / COVID-19 Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies / Screening_studies País como assunto: America do sul / Brasil Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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