The role of masks in reducing the risk of new waves of COVID-19 in low transmission settings: a modeling study

  1. RM Stuart
  2. Romesh G Abeysuriya
  3. Cliff C Kerr
  4. Dina Mistry
  5. Daniel J Klein
  6. Richard Gray
  7. Margaret Hellard
  8. Nick Scott

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Abstract

Objectives

To evaluate the risk of a new wave of coronavirus disease 2019 (COVID-19) in a setting with ongoing low transmission, high mobility, and an effective test-and-trace system, under different assumptions about mask uptake.

Design

We used a stochastic agent-based microsimulation model to create multiple simulations of possible epidemic trajectories that could eventuate over a five-week period following prolonged low levels of community transmission.

Setting

We calibrated the model to the epidemiological and policy environment in New South Wales, Australia, at the end of August 2020.

Participants

None

Intervention

From September 1, 2020, we ran the stochastic model with the same initial conditions(i.e., those prevailing at August 31, 2020), and analyzed the outputs of the model to determine the probability of exceeding a given number of new diagnoses and active cases within five weeks, under three assumptions about future mask usage: a baseline scenario of 30% uptake, a scenario assuming no mask usage, and a scenario assuming mandatory mask usage with near-universal uptake (95%).

Main outcome measure

Probability of exceeding a given number of new diagnoses and active cases within five weeks.

Results

The policy environment at the end of August is sufficient to slow the rate of epidemic growth, but may not stop the epidemic from growing: we estimate a 20% chance that NSW will be diagnosing at least 50 new cases per day within five weeks from the date of this analysis. Mandatory mask usage would reduce this to 6–9%.

Conclusions

Mandating the use of masks in community settings would significantly reduce the risk of epidemic resurgence.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.02.20186742: (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 method for constructing these networks is described in our previous study of the Victorian epidemic [13] and is based on the methodology of the SynthPops Python package [14].
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    There are several limitations to this study. Firstly, the mathematical model that we use is subject to the usual limitations of mathematical models, including uncertainty around the parameters that characterize COVID-19 transmission and disease progression, uncertainty around the impact of interventions and behavioral changes, and reliance on data sources (such as the number of COVID-19 cases by likely source) that may be incomplete and/or subject to revision. To the extent possible, we managed these issues by sampling parameters from probability distributions and conducting sensitivity analyses around the efficacy of masks. Secondly, we made assumptions about the proportion of contacts of diagnosed cases that can be traced within a certain number of days; further data on these proportions would greatly improve model estimates. Thirdly, our analyses assume that the policy environment in New South Wales would be relatively slow to react to an increase in case numbers; we focused on the question of quantifying the likelihood of diagnosing more than 50 cases/day on the assumption that this would equate to a high likelihood that New South Wales would enter a more restrictive phase of lockdown, but a faster policy reaction, as recently seen in Auckland, would change the nature of the results seen here. Our work suggests that adoption of face masks by the general public could substantially reduce the risk of new epidemic waves. Given that individuals are already requested to isolat...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2020.09.02.20186742 on medRxiv