BRAZIL IS PROJECTED TO BE THE NEXT GLOBAL COVID-19 PANDEMIC EPICENTER

  1. Pedro de Lemos Menezes
  2. David M. Garner
  3. Vitor E. Valenti

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Abstract

Coronavirus disease 2019 (COVID-19) is a disease triggered by SARS-CoV-2 infection, which is related in the most recent pandemic situation, significantly affecting health and economic systems. In this study we assessed the death rate associated to COVID-19 in Brazil and the United States of America (USA) to estimate the probability of Brazil becoming the next pandemic epicenter. We equated data between Brazil and USA obtained through the Worldometer website ( www.worldometer.info ). Epidemic curves from Brazil and USA were associated and regression analysis was undertaken to predict the Brazilian death rate regarding COVID-19 in June. In view of data from April 9 th 2020, death rates in Brazil follow a similar exponential increase to USA (r=0.999; p<0.001), estimating 64,310 deaths by June 9 th 2020. In brief, our results demonstrated that Brazil follows an analogous progression of COVID-19 deaths cases when compared to USA, signifying that Brazil could be the next global epicenter of COVID-19. We highlight public strategies to decrease the COVID-19 outbreak.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.28.20083675: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were managed via Microsoft Excel version 16.36, for MacOS Catalina version 10.15.4.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Statistical assessments were computed by IBM SPSS Statistic Subscription application 1.0.0.1347 64Bits for MacOS.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.28.20083675v1 on medRxiv