The COVID-19 pandemic, worldwide up to December 2020, caused over 1.7 million deaths, and put the world's most advanced healthcare systems under heavy stress. In many countries, drastic restrictive measures adopted by political authorities, such as national lockdowns, have not prevented the outbreak of the new pandemic's waves. In this article, we propose an integrated detection-estimation-forecasting framework that, using publicly available data, is designed to: learn relevant features of the pandemic (e.g., the infection rate); detect as quickly as possible the onset (or the termination) of an exponential growth of the contagion; and reliably forecast the pandemic evolution. The proposed solution is validated by analyzing the COVID-19 second and third waves in the United States.

Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves / Soldi G.; Forti N.; Gaglione D.; Braca P.; Millefiori L.M.; Marano S.; Willett P.K.; Pattipati K.R.. - In: IEEE COMMUNICATIONS MAGAZINE. - ISSN 0163-6804. - STAMPA. - 59:(2021), pp. 9.16-9.22. [10.1109/MCOM.101.2001252]

Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves

Forti N.;
2021

Abstract

The COVID-19 pandemic, worldwide up to December 2020, caused over 1.7 million deaths, and put the world's most advanced healthcare systems under heavy stress. In many countries, drastic restrictive measures adopted by political authorities, such as national lockdowns, have not prevented the outbreak of the new pandemic's waves. In this article, we propose an integrated detection-estimation-forecasting framework that, using publicly available data, is designed to: learn relevant features of the pandemic (e.g., the infection rate); detect as quickly as possible the onset (or the termination) of an exponential growth of the contagion; and reliably forecast the pandemic evolution. The proposed solution is validated by analyzing the COVID-19 second and third waves in the United States.
2021
59
16
22
Goal 3: Good health and well-being
Soldi G.; Forti N.; Gaglione D.; Braca P.; Millefiori L.M.; Marano S.; Willett P.K.; Pattipati K.R.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1312005
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