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https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/about http://eurovoc.europa.eu/5881 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/author https://orcid.org/0000-0002-2736-0052 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/contentSize 4647 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/contentUrl https://api.rohub.org/api/ros/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/crate/download/ https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/creator mailto:service-account-generation-service https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/dateCreated 2021-12-10 10:07:23.364596+00:00 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/dateModified 2025-03-05 01:19:14.514021+00:00 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/datePublished 2021-12-10 10:07:23.364596+00:00 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/description (1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings. https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/encodingFormat application/ld+json https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/hasPart https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/identifier https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://schema.org/name Spatiotemporal Analysis of COVID-19 Incidence Data https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://w3id.org/ro-id/rohub/model#creation_mode MANUAL https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roevo#LiveRO https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Dataset https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://w3id.org/ro/earth-science#BibliographyResearchObject https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#BibliographyResearchObject https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/ https://www.w3.org/ns/iana/link-relations/relation#cite-as Foglini, Federica. "Spatiotemporal Analysis of COVID-19 Incidence Data." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b. https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/author mailto:service-account-generation-service https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/contentSize 130 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/contentUrl https://api.rohub.org/api/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048/download/ https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/creator mailto:service-account-generation-service https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/dateCreated 2021-12-10 10:07:26.094439+00:00 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/dateModified 2021-12-10 10:07:26.095871+00:00 https://w3id.org/ro-id/56ee1bf1-115d-4e17-a33d-4cb4d37d596b/resources/c734192a-9dc2-4ea9-971a-18e99d6ea048 http://schema.org/description (1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. 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