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

Autor:	Soldi, Giovanni, Forti, Nicola, Gaglione, Domenico, Braca, Paolo, Millefiori, Leonardo M., Marano, Stefano, Willett, Peter, Pattipati, Krishna
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Signal Processing Physics - Physics and Society Quantitative Biology - Populations and Evolution
Zdroj:	IEEE Communications Magazine, vol. 59, no. 9, pp. 16-22, 2021
Druh dokumentu:	Working Paper
DOI:	10.1109/MCOM.101.2001252
Popis:	The COVID-19 pandemic has, worldwide and 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 new pandemic's waves. In this article, we propose an integrated detection-estimation-forecasting framework that, using publicly available data, is designed to: (i) learn relevant features of the pandemic (e.g., the infection rate); (ii) detect as quickly as possible the onset (or the termination) of an exponential growth of the contagion; and (iii) reliably forecast the pandemic evolution. The proposed solution is validated by analyzing the COVID-19 second and third waves in the USA. Comment: Accepted to be published in IEEE Communications Magazine, feature topic "Networking Technologies to Combat the COVID-19 Pandemic"
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.04620 Zobrazit plný text záznamu View this record from Arxiv