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  1. Article: Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities.

    Martines, M R / Ferreira, R V / Toppa, R H / Assunção, L M / Desjardins, M R / Delmelle, E M

    Journal of geographical systems

    2021  Volume 23, Issue 1, Page(s) 7–36

    Abstract: ... between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables ... both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk ... study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship ...

    Abstract The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.
    Language English
    Publishing date 2021-03-08
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1481603-9
    ISSN 1435-5949 ; 1435-5930
    ISSN (online) 1435-5949
    ISSN 1435-5930
    DOI 10.1007/s10109-020-00344-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

    Martines, Marcos Roberto / Ferreira, Ricardo Vicente / Toppa, Rogerio H / Assuncao, Luiza / Desjardins, Michael Richard / Delmelle, Eric M

    medRxiv

    Abstract: ... mortality rate, GINI index, and social inequality. We detected 11 emerging space-time clusters of COVID-19 ... space-time clusters of COVID-19. We examine the associations between clusters and mortality rate ... vulnerability, and social inequality. We used the prospective space-time scan statistic to detect daily COVID-19 ...

    Abstract The first case of COVID-19 in South America occurred in Brazil on February 25th, 2020. By June 7th, 2020, there were 691,758 confirmed cases, 36,455 confirmed deaths, and a mortality rate of 5.3%. To assist with the establishment of measures for the strategic planning to combat the COVID-19 pandemic in Brazil, we present the first Brazilian geographic study with the aims to examine active hand emerging space-time clusters of COVID-19. We examine the associations between clusters and mortality rate, vulnerability, and social inequality. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk from February 25th-June 7th, 2020 in 5,570 Brazilian municipalities. We apply a Spearman statistic to measure correlation between the relative risk of each cluster and mortality rate, GINI index, and social inequality. We detected 11 emerging space-time clusters of COVID-19 occurring in all Brazilian regions, with seven of them with a relative risk greater than one, and the highest in the Amapa state in the northern region of Brazil. We observed a positive and significant correlation between the relative risk and mortality rate, Brazilian Social Vulnerability Index, and GINI Index. The results can be utilized to improve COVID-19 response and planning in all Brazilian states.
    Keywords covid19
    Language English
    Publishing date 2020-06-16
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.06.14.20131102
    Database COVID19

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  3. Article ; Online: Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

    Martines, Marcos Roberto / Ferreira, Ricardo Vicente / Toppa, Rogerio H / Assuncao, Luiza / Desjardins, Michael Richard / Delmelle, Eric M

    Abstract: ... mortality rate, GINI index, and social inequality. We detected 11 emerging space-time clusters of COVID-19 ... space-time clusters of COVID-19. We examine the associations between clusters and mortality rate ... vulnerability, and social inequality. We used the prospective space-time scan statistic to detect daily COVID-19 ...

    Abstract The first case of COVID-19 in South America occurred in Brazil on February 25th, 2020. By June 7th, 2020, there were 691,758 confirmed cases, 36,455 confirmed deaths, and a mortality rate of 5.3%. To assist with the establishment of measures for the strategic planning to combat the COVID-19 pandemic in Brazil, we present the first Brazilian geographic study with the aims to examine active hand emerging space-time clusters of COVID-19. We examine the associations between clusters and mortality rate, vulnerability, and social inequality. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk from February 25th-June 7th, 2020 in 5,570 Brazilian municipalities. We apply a Spearman statistic to measure correlation between the relative risk of each cluster and mortality rate, GINI index, and social inequality. We detected 11 emerging space-time clusters of COVID-19 occurring in all Brazilian regions, with seven of them with a relative risk greater than one, and the highest in the Amapa state in the northern region of Brazil. We observed a positive and significant correlation between the relative risk and mortality rate, Brazilian Social Vulnerability Index, and GINI Index. The results can be utilized to improve COVID-19 response and planning in all Brazilian states.
    Keywords covid19
    Publisher MedRxiv; WHO
    Document type Article ; Online
    Note WHO #Covidence: #20131102
    DOI 10.1101/2020.06.14.20131102
    Database COVID19

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  4. Article: Detecting spacetime clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

    martines, marcos / Hartung Toppa, Rogério / Delmelle, Eric

    Journal of geographical systems, 23:7-36

    2021  

    Abstract: ... between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables ... both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk ... study to detect “active” and “emerging” spacetime clusters of COVID-19. We document the relationship ...

    Abstract The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect “active” and “emerging” spacetime clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective spacetime scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25–June 7, 2020, and February 25–July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 “active” clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.
    Keywords COVID-19 ; Geographic information systems ; Disease surveillance ; Space–time statistics ; Spatial model ; Relative risk
    Language English
    Document type Article
    Database Repository for Life Sciences

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