Assessing the relative contributions of healthcare protocols for epidemic control: an example with network transmission model for COVID-19

  1. Matheus Tenório Baumgartner
  2. Fernando Miranda Lansac-Tôha

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Abstract

The increasing number of COVID-19 cases threatens human life and requires retainment actions that control the spread of the virus in the absence of effective medical therapy or a reliable vaccine. There is a general consensus that the most efficient health protocol in the actual state is to disrupt the infection chain through social distancing, although economic interests stand against closing non-essential activities and poses a debatable tradeoff. In this study, we used an individual-based age-structured network model to assess the effective roles of different healthcare protocols such as the use of personal protection equipment and social distancing at neighbor- and city-level scales. Using as much as empirical data available in the literature, we calibrated a city model and simulated low, medium, and high parameters representing these protocols. Our results revealed that the model was more sensitive to changes in the parameter representing the rate of contact among people from different neighborhoods, which defends the social distancing at the city-level as the most effective protocol for the control of the disease outbreak. Another important identified parameter represented the use of individual equipment such as masks, face shields, and hand sanitizers like alcohol-based solutions and antiseptic products. Interestingly, our simulations suggest that some periodical activities such as going to the supermarket, gas station, and pharmacy would have little contribution to the SARS-CoV-2 spread once performed within the same neighborhood. As we can see nowadays, there is an inevitable context-dependency and economic pressure on the level of social distancing recommendations, and we reinforce that every decision must be a welfare-oriented science-based decision.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.20.20158576: (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
    Empirical healthcare data: To infer about prioritizing the efforts of public health personnel, we compared the outcomes of simulations with the nominal carrying capacity of healthcare facilities potentially able to treat COVID-19 cases in Maringá (Fig. S4).
    Empirical healthcare
    suggested: (VISN 4 MIRECC, RRID:SCR_001970)

    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: We detected the following sentences addressing limitations in the study:
    Just like any model, ours have limitations as well. First, we used a fixed number of hospital beds, which is certainly unrealistic if we consider that there is a current effort to expand the nominal carrying capacity of these facilities in many cities (Croda et al., 2020). However, no matter what estimate we use to forecast the deficit in the number of available beds, projections show that there will not be enough ventilators to treat COVID-19 patients in the next few months, even in the best-case scenarios (Ranney et al., 2020). Second, we did not account for time-varying or dynamic public health protocols. As we can see nowadays, there is an inevitable context-dependency and economic pressure on the level of social distancing recommendations. Certainly, models with a real-time structure accounting for this dynamic would be more appropriate to build an evidence-based political framework.

    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 10.1101/2020.07.20.20158576v1 on medRxiv