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A perspective on early detection systems models for COVID-19 spreading.
Vianello, Chiara; Strozzi, Fernanda; Mocellin, Paolo; Cimetta, Elisa; Fabiano, Bruno; Manenti, Flavio; Pozzi, Rossella; Maschio, Giuseppe.
Afiliación
  • Vianello C; Università Degli Studi di Padova, Dipartimento di Ingegneria Industriale. Via Marzolo 9, 35131, Padova, Italy. Electronic address: chiara.vianello@unipd.it.
  • Strozzi F; Università Carlo Cattaneo - LIUC. Corso Matteotti 22, 21053, Castellanza (Varese), Italy.
  • Mocellin P; Università Degli Studi di Padova, Dipartimento di Ingegneria Industriale. Via Marzolo 9, 35131, Padova, Italy.
  • Cimetta E; Università Degli Studi di Padova, Dipartimento di Ingegneria Industriale. Via Marzolo 9, 35131, Padova, Italy; Fondazione Istituto di Ricerca Pediatrica Città Della Speranza, Corso Stati Uniti 4, 35127, Padova, Italy.
  • Fabiano B; Università di Genova, Dipartimento di Ingegneria Civile, Chimica e Ambientale. Via Montallegro 1, 15145, Genova, Italy.
  • Manenti F; Politecnico di Milano. CMIC Dept. "Giulio Natta", Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
  • Pozzi R; Università Carlo Cattaneo - LIUC. Corso Matteotti 22, 21053, Castellanza (Varese), Italy.
  • Maschio G; Università Degli Studi di Padova, Dipartimento di Ingegneria Industriale. Via Marzolo 9, 35131, Padova, Italy.
Biochem Biophys Res Commun ; 538: 244-252, 2021 01 29.
Article en En | MEDLINE | ID: mdl-33342518
The ongoing COVID-19 epidemic highlights the need for effective tools capable of predicting the onset of infection outbreaks at their early stages. The tracing of confirmed cases and the prediction of the local dynamics of contagion through early indicators are crucial measures to a successful fight against emerging infectious diseases (EID). The proposed framework is model-free and applies Early Warning Detection Systems (EWDS) techniques to detect changes in the territorial spread of infections in the very early stages of onset. This study uses publicly available raw data on the spread of SARS-CoV-2 mainly sourced from the database of the Italian Civil Protection Department. Two distinct EWDS approaches, the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z), are adapted and applied to the current SARS-CoV-2 outbreak. They promptly generate warning signals and detect the onset of an epidemic at early surveillance stages even if working on the limited daily available, open-source data. Additionally, EWDS S&Z criterion is theoretically validated on the basis of the epidemiological SIR. Discussed EWDS successfully analyze self-accelerating systems, like the SARS-CoV-2 scenario, to precociously identify an epidemic spread through the calculation of onset parameters. This approach can also facilitate early clustering detection, further supporting common fight strategies against the spread of EIDs. Overall, we are presenting an effective tool based on solid scientific and methodological foundations to be used to complement medical actions to contrast the spread of infections such as COVID-19.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Brotes de Enfermedades / Monitoreo Epidemiológico / SARS-CoV-2 / COVID-19 Tipo de estudio: Diagnostic_studies / Prognostic_studies / Screening_studies Límite: Humans Idioma: En Revista: Biochem Biophys Res Commun Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Brotes de Enfermedades / Monitoreo Epidemiológico / SARS-CoV-2 / COVID-19 Tipo de estudio: Diagnostic_studies / Prognostic_studies / Screening_studies Límite: Humans Idioma: En Revista: Biochem Biophys Res Commun Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos