Mathematical model of COVID-19 intervention scenarios for São Paulo—Brazil

  1. Osmar Pinto Neto
  2. Deanna M. Kennedy
  3. José Clark Reis
  4. Yiyu Wang
  5. Ana Carolina Brisola Brizzi
  6. Gustavo José Zambrano
  7. Joabe Marcos de Souza
  8. Wellington Pedroso
  9. Rodrigo Cunha de Mello Pedreiro
  10. Bruno de Matos Brizzi
  11. Ellysson Oliveira Abinader
  12. Renato Amaro Zângaro

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Abstract

With COVID-19 surging across the world, understanding the effectiveness of intervention strategies on transmission dynamics is of primary global health importance. Here, we develop and analyze an epidemiological compartmental model using multi-objective genetic algorithm design optimization to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in São Paulo, Brazil. The results indicate that the optimal strategy for São Paulo is to reduce social distancing over time with a stepping-down reduction in the magnitude of social distancing every 80-days. Our results also indicate that the ability to reduce social distancing depends on a 5–10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic. Our framework can be extended to model transmission dynamics for other countries, regions, states, cities, and organizations.

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  1. Version published to 10.1038/s41467-020-20687-y
  2. ScreenIT

    SciScore for 10.1101/2020.04.26.20081208: (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
    Fitting analysis was done with a custom build MATLAB global optimization algorithm using Monte Carlo iterations and multiple local minima searches (Figure 1).
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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 10.1101/2020.04.26.20081208v2 on medRxiv
  4. Version published to 10.21203/rs.3.rs-32962/v1 on Research Square
  5. Version published to 10.1101/2020.04.26.20081208v1 on medRxiv