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- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b 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." assertion.
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- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b dateCreated "2021-12-10 10:07:23.364596+00:00" assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b creation_mode "MANUAL" assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b 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." assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b about 5881 assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b name "Spatiotemporal Analysis of COVID-19 Incidence Data" assertion.
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- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b creator mailto:service-account-generation-service assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b dateModified "2025-03-05 01:19:14.514021+00:00" assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b datePublished "2021-12-10 10:07:23.364596+00:00" assertion.
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- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b license no-permission assertion.
- 56ee1bf1-115d-4e17-a33d-4cb4d37d596b author 0000-0002-2736-0052 assertion.