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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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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. |
format | Online Article Text |
id | pubmed-8600919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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