Spreading of COVID-19 in Brazil: Impacts and uncertainties in social distancing strategies

  1. Diego T. Volpatto
  2. Anna Claudia M. Resende
  3. Lucas dos Anjos
  4. João V. O. Silva
  5. Claudia M. Dias
  6. Regina C. Almeida
  7. Sandra M. C. Malta

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Abstract

Brazil’s continental dimension poses a challenge to the control of the spread of COVID-19. Due to the country specific scenario of high social and demographic heterogeneity, combined with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy, the focus of this study is twofold: (i) to develop a generalized SEIRD model that implicitly takes into account the quarantine measures, and (ii) to estimate the response of the COVID-19 spread dynamics to perturbations/uncertainties. By investigating the projections of cumulative numbers of confirmed and death cases, as well as the effective reproduction number, we show that the model parameter related to social distancing measures is one of the most influential along all stages of the disease spread and the most influential after the infection peak. Due to such importance in the outcomes, different relaxation strategies of social distancing measures are investigated in order to determine which strategies are viable and less hazardous to the population. The results highlight the need of keeping social distancing policies to control the disease spread. Specifically, the considered scenario of abrupt social distancing relaxation implemented after the occurrence of the peak of positively diagnosed cases can prolong the epidemic, with a significant increase of the projected numbers of confirmed and death cases. An even worse scenario could occur if the quarantine relaxation policy is implemented before evidence of the epidemiological control, indicating the importance of the proper choice of when to start relaxing social distancing measures.

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  1. Version published to 10.1101/2020.05.30.20117283v2 on medRxiv
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    SciScore for 10.1101/2020.05.30.20117283: (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
    The code implementation is written in Python language (version 3.7), using PyMC3 as a Probabilistic Programming framework [13].
    Python
    suggested: (IPython, RRID:SCR_001658)
    Of note, the SEAIRPD-Q system of seven ordinary differential equations is solved using the LSODA method [16] from SciPy [17].
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Values of p as an even number and are usual suggested choices to ensure an equal sampling probability in the parametric space, and are the default option in the SALib library [21].
    SALib
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    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 10.1101/2020.05.30.20117283v1 on medRxiv