Predictive model of COVID-19 incidence and socioeconomic description of municipalities in Brazil

  1. Isadora C. R. Carneiro
  2. Eloiza D. Ferreira
  3. Janaina C. da Silva
  4. Guilherme Soares
  5. Daisy M. Strottmann
  6. Guilherme F. Silveira

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Abstract

Coronaviruses are enveloped viruses that can cause respiratory, gastrointestinal, hepatic, and neurological diseases. In December 2019, a new highly contagious coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China. SARS-CoV-2 causes a potentially lethal human respiratory infection, COVID-19, that is associated with fever and cough and can progress to pneumonia and dyspnea in severe cases. Since the virus emerged, it has spread rapidly, reaching all continents around the world. A previous study has shown that, despite being the best alternative in the current pandemic context, social distancing measures alone may not be sufficient to prevent COVID-19 spread, and the overall impact of the virus is of great concern. The present study aims to describe the demographic and socioeconomic characteristics of 672 cities with cases of COVID-19, as well as to determine a predictive model for the number of cases. We analyzed data from cities with at least 1 reported case of COVID-19 until June 26, 2020. It was observed that cities with confirmed cases of the disease are present in all Brazilian states, affecting 36.5% of the municipalities in Rio de Janeiro State. The inhabitants in cities with reported cases of COVID-19 represent more than 73.1% of the Brazilian population. Stratifying the age groups of the inhabitants and accounting for the percentage of women and men does not affect COVID-19 incidence (confirmed cases/100,000 inhabitants). The demographic density, the MHDI and the per capita income of the municipalities with cases of COVID-19 do not affect disease incidence. In addition, if conditions are maintained, our model predicts 2,358,703 (2,172,930 to 2,544,477) cumulative cases on July 25, 2020.

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    SciScore for 10.1101/2020.06.28.20141952: (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

    Software and Algorithms
    SentencesResources
    Along with Python, it was necessary to import different packages and libraries, the most used ones being pandas, NumPy and SciPy, with the function of organizing and structuring the data.
    Python
    suggested: (IPython, RRID:SCR_001658)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Matplotlib and Seaborn were used to generate two-dimensional (2D) graphics.
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    Results from OddPub: Thank you for sharing your code.

    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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  2. Version published to 10.1101/2020.06.28.20141952v1 on medRxiv