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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: Fioriti, Vincenzo, Chinnici, Marta, Arbore, Andrea, Sigismondi, Nicola, Roselli, Ivan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600919/
https://www.ncbi.nlm.nih.gov/pubmed/34816052
http://dx.doi.org/10.1016/j.heliyon.2021.e08422
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author Fioriti, Vincenzo
Chinnici, Marta
Arbore, Andrea
Sigismondi, Nicola
Roselli, Ivan
author_facet Fioriti, Vincenzo
Chinnici, Marta
Arbore, Andrea
Sigismondi, Nicola
Roselli, Ivan
author_sort Fioriti, Vincenzo
collection PubMed
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.
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spelling pubmed-86009192021-11-18 Estimating the epidemic growth dynamics within the first week Fioriti, Vincenzo Chinnici, Marta Arbore, Andrea Sigismondi, Nicola Roselli, Ivan Heliyon Research Article 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. Elsevier 2021-11-18 /pmc/articles/PMC8600919/ /pubmed/34816052 http://dx.doi.org/10.1016/j.heliyon.2021.e08422 Text en © 2021 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Fioriti, Vincenzo
Chinnici, Marta
Arbore, Andrea
Sigismondi, Nicola
Roselli, Ivan
Estimating the epidemic growth dynamics within the first week
title 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_short Estimating the epidemic growth dynamics within the first week
title_sort estimating the epidemic growth dynamics within the first week
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600919/
https://www.ncbi.nlm.nih.gov/pubmed/34816052
http://dx.doi.org/10.1016/j.heliyon.2021.e08422
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