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Spatial pattern of COVID-19 deaths and infections in small areas of Brazil

Author

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  • Everton Emanuel Campos de Lima
  • Ezra Gayawan
  • Emerson Augusto Baptista
  • Bernardo Lanza Queiroz

Abstract

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths.

Suggested Citation

  • Everton Emanuel Campos de Lima & Ezra Gayawan & Emerson Augusto Baptista & Bernardo Lanza Queiroz, 2021. "Spatial pattern of COVID-19 deaths and infections in small areas of Brazil," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0246808
    DOI: 10.1371/journal.pone.0246808
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    References listed on IDEAS

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    1. Lima, Everton & Vilela, Estevão & Peralta, Andrés & Rocha, Marília Gabriela & Queiroz, Bernardo L & Gonzaga, Marcos Roberto & Freire, Flávio & Piscoya, Mario, 2020. "Exploring excess of deaths in the context of covid pandemic in selected countries of Latin America," OSF Preprints xhkp4, Center for Open Science.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Anthony Medford & Sergi Trias-Llimós, 2020. "Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(19), pages 533-544.
    4. James P. Lesage, 1997. "Bayesian Estimation of Spatial Autoregressive Models," International Regional Science Review, , vol. 20(1-2), pages 113-129, April.
    5. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    6. Emerson Baptista & Bernardo Lanza Queiroz, 2019. "The relation between cardiovascular mortality and development: Study for small areas in Brazil, 2001–2015," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(51), pages 1437-1452.
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    1. Balázs PAGER & Csaba G. TOTH & Annamária UZZOLI, 2024. "The role of socioeconomic variables in the regional inequalities of COVID-19 mortality in Hungary," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 15, pages 272-297, June.
    2. Megan Ruth Laxton & Glenna Nightingale & Finn Lindgren & Arjuna Sivakumaran & Richard Othieno, 2023. "Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in student," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-20, November.

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