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Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy

BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely...

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Autores principales: Giuliani, Diego, Dickson, Maria Michela, Espa, Giuseppe, Santi, Flavio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509829/
https://www.ncbi.nlm.nih.gov/pubmed/32967639
http://dx.doi.org/10.1186/s12879-020-05415-7
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author Giuliani, Diego
Dickson, Maria Michela
Espa, Giuseppe
Santi, Flavio
author_facet Giuliani, Diego
Dickson, Maria Michela
Espa, Giuseppe
Santi, Flavio
author_sort Giuliani, Diego
collection PubMed
description BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time. METHODS: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. RESULTS: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting. CONCLUSIONS: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished.
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spelling pubmed-75098292020-09-23 Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy Giuliani, Diego Dickson, Maria Michela Espa, Giuseppe Santi, Flavio BMC Infect Dis Research Article BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time. METHODS: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. RESULTS: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting. CONCLUSIONS: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished. BioMed Central 2020-09-23 /pmc/articles/PMC7509829/ /pubmed/32967639 http://dx.doi.org/10.1186/s12879-020-05415-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Giuliani, Diego
Dickson, Maria Michela
Espa, Giuseppe
Santi, Flavio
Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy
title Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy
title_full Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy
title_fullStr Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy
title_full_unstemmed Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy
title_short Modelling and predicting the spatio-temporal spread of cOVID-19 in Italy
title_sort modelling and predicting the spatio-temporal spread of covid-19 in italy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509829/
https://www.ncbi.nlm.nih.gov/pubmed/32967639
http://dx.doi.org/10.1186/s12879-020-05415-7
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