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Spatio-temporal small area surveillance of the COVID-19 pandemic.
Martinez-Beneito, Miguel A; Mateu, Jorge; Botella-Rocamora, Paloma.
  • Martinez-Beneito MA; Department of Statistics and Operations Research, University of Valencia, Burjassot (Valencia), Spain.
  • Mateu J; Unitat Mixta de recerca en mètodes estadístics per a dades biomédiques i sanitàries, UV-FISABIO, Spain.
  • Botella-Rocamora P; Department of Mathematics, University Jaume I of Castellon, Castelló de la Plana, Spain.
Spat Stat ; 49: 100551, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1889892
ABSTRACT
The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number R t , even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating and monitoring R t for small areas. We illustrate our proposal on the study of the disease pandemic in two Spanish regions. As a result, we show how tourism flows have shaped the spatial distribution of the disease in these regions. In these case studies, we also develop new epidemiological tools to be used by regional public health services for small area surveillance.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Screening study Topics: Variants Language: English Journal: Spat Stat Year: 2022 Document Type: Article Affiliation country: J.spasta.2021.100551

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Screening study Topics: Variants Language: English Journal: Spat Stat Year: 2022 Document Type: Article Affiliation country: J.spasta.2021.100551