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Quickest Detection of COVID-19 Pandemic Onset.
Braca, P; Gaglione, D; Marano, S; Millefiori, L M; Willett, P; Pattipati, K.
Afiliação
  • Braca P; NATO STO CMRE, Research Department, La Spezia, 19126, Italy.
  • Gaglione D; NATO STO CMRE, Research Department, La Spezia, 19126, Italy.
  • Marano S; DIEM, University of Salerno, Italy.
  • Millefiori LM; NATO STO CMRE, Research Department, La Spezia, 19126, Italy.
  • Willett P; ECE Dept., University of Connecticut, Storrs, 06269, USA.
  • Pattipati K; ECE Dept., University of Connecticut, Storrs, 06269, USA.
IEEE Signal Process Lett ; 28: 683-687, 2021.
Article em En | MEDLINE | ID: mdl-34163125
ABSTRACT
This paper develops an easily-implementable version of Page's CUSUM quickest-detection test, designed to work in certain composite hypothesis scenarios with time-varying data statistics. The decision statistic can be cast in a recursive form and is particularly suited for on-line analysis. By back-testing our approach on publicly-available COVID-19 data we find reliable early warning of infection flare-ups, in fact sufficiently early that the tool may be of use to decision-makers on the timing of restrictive measures that may in the future need to be taken.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: IEEE Signal Process Lett Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Itália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: IEEE Signal Process Lett Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Itália