New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy

Abstract COVID-19 pandemic in Italy displayed a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of COVID-19 diffusion. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian...

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Autores principales:	Mariano Bizzarri, Mario Di Traglia, Alessandro Giuliani, Annarita Vestri, Valeria Fedeli, Alberto Prestininzi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/45c17023e71543aa88be9e9b215426a5
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spelling	oai:doaj.org-article:45c17023e71543aa88be9e9b215426a52021-12-02T13:56:50ZNew statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy10.1038/s41598-020-79039-x2045-2322https://doaj.org/article/45c17023e71543aa88be9e9b215426a52020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79039-xhttps://doaj.org/toc/2045-2322Abstract COVID-19 pandemic in Italy displayed a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of COVID-19 diffusion. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian population). Moreover, 86% of fatalities concentrated in four Northern Italy regions. The ‘explosive’ outbreak of COVID-19 in Lombardia at the very beginning of pandemic fatally biased the R-like statistics routinely used to control the disease dynamics. To (at least partially) overcome this bias, we propose a new index RI = dH/dI (daily derivative ratio of H and I, given H = Healed and I = Infected), corresponding to the ratio between healed and infected patients relative daily changes. The proposed index is less flawed than R by the uncertainty related to the estimated number of infected persons and allows to follow (and possibly forecast) epidemic dynamics in a largely model-independent way. To analyze the dynamics of the epidemic, starting from the beginning of the virus spreading—when data are insufficient to make an estimate by adopting SIR model—a "sigmoidal family with delay" logistic model was introduced. That approach allowed in estimating the epidemic peak using the few data gathered even before mid-March. Based on this analysis, the peak was correctly predicted to occur by end of April. Analytical methodology of the dynamics of the epidemic we are proposing herein aims to forecast the time and intensity of the epidemic peak (forward prediction), while allowing identifying the (more likely) beginning of the epidemic (backward prediction). In addition, we established a relationship between hospitalization in intensive care units (ICU) versus deaths daily rates by avoiding the necessity to rely on precise estimates of the infected fraction of the population The joint evolution of the above parameters over time allows for a trustworthy and unbiased estimation of the dynamics of the epidemic, allowing us to clearly detect the qualitatively different character of the ‘so-called’ second wave with respect to the previous epidemic peak.Mariano BizzarriMario Di TragliaAlessandro GiulianiAnnarita VestriValeria FedeliAlberto PrestininziNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mariano Bizzarri Mario Di Traglia Alessandro Giuliani Annarita Vestri Valeria Fedeli Alberto Prestininzi New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy
description	Abstract COVID-19 pandemic in Italy displayed a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of COVID-19 diffusion. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian population). Moreover, 86% of fatalities concentrated in four Northern Italy regions. The ‘explosive’ outbreak of COVID-19 in Lombardia at the very beginning of pandemic fatally biased the R-like statistics routinely used to control the disease dynamics. To (at least partially) overcome this bias, we propose a new index RI = dH/dI (daily derivative ratio of H and I, given H = Healed and I = Infected), corresponding to the ratio between healed and infected patients relative daily changes. The proposed index is less flawed than R by the uncertainty related to the estimated number of infected persons and allows to follow (and possibly forecast) epidemic dynamics in a largely model-independent way. To analyze the dynamics of the epidemic, starting from the beginning of the virus spreading—when data are insufficient to make an estimate by adopting SIR model—a "sigmoidal family with delay" logistic model was introduced. That approach allowed in estimating the epidemic peak using the few data gathered even before mid-March. Based on this analysis, the peak was correctly predicted to occur by end of April. Analytical methodology of the dynamics of the epidemic we are proposing herein aims to forecast the time and intensity of the epidemic peak (forward prediction), while allowing identifying the (more likely) beginning of the epidemic (backward prediction). In addition, we established a relationship between hospitalization in intensive care units (ICU) versus deaths daily rates by avoiding the necessity to rely on precise estimates of the infected fraction of the population The joint evolution of the above parameters over time allows for a trustworthy and unbiased estimation of the dynamics of the epidemic, allowing us to clearly detect the qualitatively different character of the ‘so-called’ second wave with respect to the previous epidemic peak.
format	article
author	Mariano Bizzarri Mario Di Traglia Alessandro Giuliani Annarita Vestri Valeria Fedeli Alberto Prestininzi
author_facet	Mariano Bizzarri Mario Di Traglia Alessandro Giuliani Annarita Vestri Valeria Fedeli Alberto Prestininzi
author_sort	Mariano Bizzarri
title	New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy
title_short	New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy
title_full	New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy
title_fullStr	New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy
title_full_unstemmed	New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy
title_sort	new statistical ri index allow to better track the dynamics of covid-19 outbreak in italy
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/45c17023e71543aa88be9e9b215426a5
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New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy

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