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

Author

Listed:
  • M. R. Martines

    (Federal University of São Carlos)

  • R. V. Ferreira

    (Federal University of Triângulo Mineiro)

  • R. H. Toppa

    (Federal University of São Carlos)

  • L. M. Assunção

    (State University of Minas Gerais)

  • M. R. Desjardins

    (Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health)

  • E. M. Delmelle

    (Center for Applied Geographic Information Science, University of North Carolina at Charlotte
    University of Eastern Finland)

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.

Suggested Citation

  • M. R. Martines & R. V. Ferreira & R. H. Toppa & L. M. Assunção & M. R. Desjardins & E. M. Delmelle, 2021. "Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities," Journal of Geographical Systems, Springer, vol. 23(1), pages 7-36, January.
    • Handle: RePEc:kap:jgeosy:v:23:y:2021:i:1:d:10.1007_s10109-020-00344-0
    DOI: 10.1007/s10109-020-00344-0
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    References listed on IDEAS

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    1. Xiaolan Wu & Tony Grubesic, 2010. "Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm," Journal of Geographical Systems, Springer, vol. 12(4), pages 409-433, December.
    2. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    3. Geoffrey M. Jacquez & Dunrie A. Greiling & Andrew M. Kaufmann, 2005. "Design and implementation of a Space-Time Intelligence System for disease surveillance," Journal of Geographical Systems, Springer, vol. 7(1), pages 7-23, October.
    4. Pedro S Peixoto & Diego Marcondes & Cláudia Peixoto & Sérgio M Oliva, 2020. "Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-23, July.
    5. Leonardo Costa Ribeiro & Américo Tristão Bernardes, 2020. "Estimate of underreporting of COVID-19 in Brazil by Acute Respiratory Syndrome hospitalization reports," Notas Técnicas Cedeplar-UFMG 010, Cedeplar, Universidade Federal de Minas Gerais.
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    Cited by:

    1. R. V. Goncharov & E. A. Kotov & V. A. Molodtsova, 2024. "Local Factors of COVID-19 Severity in Russian Urban Areas," Regional Research of Russia, Springer, vol. 14(2), pages 227-239, June.
    2. Peter Congdon, 2022. "A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates," Journal of Geographical Systems, Springer, vol. 24(4), pages 583-610, October.
    3. Boris Nikitin & Maria Zakharova & Alexander Pilyasov & Nadezhda Zamyatina, 2023. "The burden of big spaces: Russian regions and cities in the COVID-19 pandemic," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-22, December.

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    More about this item

    Keywords

    Disease surveillance; COVID-19; Geographic information systems; Relative risk; Space–time statistics; Spatial models;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I10 - Health, Education, and Welfare - - Health - - - General
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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