Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil

  1. Enner Alcântara
  2. José Mantovani
  3. Luiz Rotta
  4. Edward Park
  5. Thanan Rodrigues
  6. Fernando Campos Carvalho
  7. Carlos Roberto Souza Filho

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Abstract

As of 16 May 2020, the number of confirmed cases and deaths in Brazil due to COVID-19 hit 233,142 and 15,633, respectively, making the country one of the most affected by the pandemic. The State of São Paulo (SSP) hosts the largest number of confirmed cases in Brazil, with over 60,000 cases to date. Here we investigate the spatial distribution and spreading patterns of COVID-19 in the SSP by mapping the spatial autocorrelation and the clustering patterns of the virus in relation to the population density and the number of hospital beds. Clustering analysis indicated that São Paulo City is a significant hotspot for both the confirmed cases and deaths, whereas other cities across the state were less affected. Bivariate Moran’s I showed a low relationship between the number of deaths and population density, whereas the number of hospital beds was less related, implying that the fatality depends substantially on the actual patients’ conditions. Multivariate Local Geary showed a positive relationship between the number of deaths and population density, with two cities near São Paulo City being negatively related; the relationship between the number of deaths and hospital beds availability in the São Paulo Metropolitan Area was basically positive. Social isolation measures throughout the State of São Paulo have been gradually increasing since early March, an action that helped to slow down the emergence of the new confirmed cases, highlighting the importance of the safe-distancing measures in mitigating the local transmission within and between cities in the state.

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  1. Version published to 10.4081/gh.2020.925
  2. ScreenIT

    SciScore for 10.1101/2020.05.28.20115626: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.05.28.20115626v1 on medRxiv