Estimating the epidemic growth dynamics within the first week

Information about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of c...

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Autores principales:	Vincenzo Fioriti, Marta Chinnici, Andrea Arbore, Nicola Sigismondi, Ivan Roselli
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Complex network Dynamical systems Graph theory Big data Epidemic spreading Infective diseases Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/8b7a04cc688b4f5c98f29406092c42ca
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id	oai:doaj.org-article:8b7a04cc688b4f5c98f29406092c42ca
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spelling	oai:doaj.org-article:8b7a04cc688b4f5c98f29406092c42ca2021-12-02T05:03:09ZEstimating the epidemic growth dynamics within the first week2405-844010.1016/j.heliyon.2021.e08422https://doaj.org/article/8b7a04cc688b4f5c98f29406092c42ca2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021025251https://doaj.org/toc/2405-8440Information about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of course, the crucial point is that we do not have enough data during the initial outbreak phase to make reliable inferences. Here we propose a straightforward methodology to estimate the epidemic growth dynamic from the cumulative infected data of just a week, provided a surveillance system is available over the whole territory. The methodology, based on the Newcomb-Benford Law, is applied to the Italian covid 19 case-study. Results show that it is possible to discriminate the epidemic dynamics using the first seven data points collected in fifty Italian cities. Moreover, the most probable approximating function of the growth within a six-week epidemic scenario is identified.Vincenzo FioritiMarta ChinniciAndrea ArboreNicola SigismondiIvan RoselliElsevierarticleComplex networkDynamical systemsGraph theoryBig dataEpidemic spreadingInfective diseasesScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08422- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Complex network Dynamical systems Graph theory Big data Epidemic spreading Infective diseases Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	Complex network Dynamical systems Graph theory Big data Epidemic spreading Infective diseases Science (General) Q1-390 Social sciences (General) H1-99 Vincenzo Fioriti Marta Chinnici Andrea Arbore Nicola Sigismondi Ivan Roselli Estimating the epidemic growth dynamics within the first week
description	Information about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of course, the crucial point is that we do not have enough data during the initial outbreak phase to make reliable inferences. Here we propose a straightforward methodology to estimate the epidemic growth dynamic from the cumulative infected data of just a week, provided a surveillance system is available over the whole territory. The methodology, based on the Newcomb-Benford Law, is applied to the Italian covid 19 case-study. Results show that it is possible to discriminate the epidemic dynamics using the first seven data points collected in fifty Italian cities. Moreover, the most probable approximating function of the growth within a six-week epidemic scenario is identified.
format	article
author	Vincenzo Fioriti Marta Chinnici Andrea Arbore Nicola Sigismondi Ivan Roselli
author_facet	Vincenzo Fioriti Marta Chinnici Andrea Arbore Nicola Sigismondi Ivan Roselli
author_sort	Vincenzo Fioriti
title	Estimating the epidemic growth dynamics within the first week
title_short	Estimating the epidemic growth dynamics within the first week
title_full	Estimating the epidemic growth dynamics within the first week
title_fullStr	Estimating the epidemic growth dynamics within the first week
title_full_unstemmed	Estimating the epidemic growth dynamics within the first week
title_sort	estimating the epidemic growth dynamics within the first week
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/8b7a04cc688b4f5c98f29406092c42ca
work_keys_str_mv	AT vincenzofioriti estimatingtheepidemicgrowthdynamicswithinthefirstweek AT martachinnici estimatingtheepidemicgrowthdynamicswithinthefirstweek AT andreaarbore estimatingtheepidemicgrowthdynamicswithinthefirstweek AT nicolasigismondi estimatingtheepidemicgrowthdynamicswithinthefirstweek AT ivanroselli estimatingtheepidemicgrowthdynamicswithinthefirstweek
_version_	1718400723144671232

Estimating the epidemic growth dynamics within the first week

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