About: Modelling and predicting the spatio-temporal spread of Coronavirus disease 2019 (COVID-19) in Italy

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About: Modelling and predicting the spatio-temporal spread of Coronavirus disease 2019 (COVID-19) in Italy Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Modelling and predicting the spatio-temporal spread of Coronavirus disease 2019 (COVID-19) in Italy
Creator	Dickson, Maria Espa, Giuseppe Giuliani, Diego Santi, Flavio
source	ArXiv
abstract	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. Data time horizon ranges from 26 February 20020, which is the date when the first case not directly connected with China has been discovered in northern Italy, to 18 March 2020. An endemic-epidemic multivariate time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. Previous literature has shown that these class of models provide reliable predictions of infectious diseases in time and space. Three subcomponents characterize the estimated model. The first is related to the evolution of the disease over time; the second is characterized by transmission of the illness among inhabitants of the same province; the third remarks the effects of spatial neighbourhood and try to capture the contagion effects of nearby areas. Focusing on the aggregated time-series of the daily counts in Italy, the contribution of any of the three subcomponents do not dominate on the others and our predictions are excellent for the whole country, with an error of 3 per thousand compared to the late available data. At local level, instead, interesting distinct patterns emerge. In particular, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, for the provinces the are currently strongly affected by contagions, the component accounting for the spatial interaction with surrounding areas is prevalent. Moreover, the proposed model provides good forecasts of the number of infections at local level while controlling for delayed reporting.
has issue date	2020-03-14(xsd:dateTime)
has license	arxiv
sha1sum (hex)	e112de47dc1b3af7841057fbc398e8f24a2f1eb5
resource representing a document's title	Modelling and predicting the spatio-temporal spread of Coronavirus disease 2019 (COVID-19) in Italy
resource representing a document's body	covid:e112de47dc1b3af7841057fbc398e8f24a2f1eb5#body_text
is schema:about of	named entity 'Italy' named entity 'random effects' named entity 'Il Sole 24 ore' named entity 'contagions' named entity 'province of Lodi' »more»

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