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Applying Spatio-temporal Scan Statistics and Spatial Autocorrelation Statistics to identify Covid-19 clusters in the world - A Vaccination Strategy?
Morais, Lucas Rabelo de Araújo; Gomes, Gecynalda Soares da Silva.
  • Morais LRA; Department of Statistics, Federal University of Bahia (UFBA), Salvador, Brazil. Electronic address: lucas.rabelo@ufba.br.
  • Gomes GSDS; Department of Statistics, Federal University of Bahia (UFBA), Salvador, Brazil.
Spat Spatiotemporal Epidemiol ; 39: 100461, 2021 11.
Article in English | MEDLINE | ID: covidwho-1510319
ABSTRACT
With the whole world being affected by the pandemic, it is a matter of great importance that studies about spatial and spatio-temporal aspects of the COVID-19 (Sars-Cov-2) pandemic should be conducted, therefore the main goal of this paper is to present the Global Moran's I and the Local Moran's I used to evaluate spatial association in the number of deaths and infections by COVID-19, and a spatio-temporal Poisson scan statistic used to identify emerging or "alive" clusters of infections by Sars-Cov-2 in space and time. As of January 2021 vaccination against COVID-19 already started, since the use of spatial clustering methods to identify non-vaccinated populations is not new among studies on vaccination coverage strategies, this paper also aims to discuss the implementation of spatial and spatio-temporal clustering methods in early vaccination.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies Topics: Vaccines Limits: Humans Language: English Journal: Spat Spatiotemporal Epidemiol Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies Topics: Vaccines Limits: Humans Language: English Journal: Spat Spatiotemporal Epidemiol Year: 2021 Document Type: Article