Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model

  1. Junjiang Li
  2. Philippe J. Giabbanelli

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Abstract

There is a range of public health tools and interventions to address the global pandemic of COVID-19. Although it is essential for public health efforts to comprehensively identify which interventions have the largest impact on preventing new cases, most of the modeling studies that support such decision-making efforts have only considered a very small set of interventions. In addition, previous studies predominantly considered interventions as independent or examined a single scenario in which every possible intervention was applied. Reality has been more nuanced, as a subset of all possible interventions may be in effect for a given time period, in a given place. In this paper, we use cloud-based simulations and a previously published Agent-Based Model of COVID-19 ( Covasim ) to measure the individual and interacting contribution of interventions on reducing new infections in the US over 6 months. Simulated interventions include face masks, working remotely, stay-at-home orders, testing, contact tracing, and quarantining. Through a factorial design of experiments, we find that mask wearing together with transitioning to remote work/schooling has the largest impact. Having sufficient capacity to immediately and effectively perform contact tracing has a smaller contribution, primarily via interacting effects.

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    SciScore for 10.1101/2020.12.11.20247825: (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

    No key resources detected.

    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 modeling study. First, we are unable to report the contribution of the error factor in the factorial design, that is, the margin due to stochasticity in the model. Evaluating it would require a comparable number of samples for each combination of parameter values, but this number differed greatly among experiments as the 95% Confidence Interval required many more runs for some combinations than others. Running all experiments with the highest number of replications was computational unfeasible, and the wide differences in number of runs precluded a reliable up- or down-sampling. Second, as any model is a simplification of reality, some factors are not represented in the underlying COVID-19 ABM used for this study. For instance, the underlying model does not take into account the duration of immunity, which may be in the scale of a few months depending on initial illness severity [14]. We assumed that immunity would hold during the 6 months (180 days) of simulated time. Finally, although our parameter values were obtained from peer-reviewed sources, the evidence base on COVID-19 continues to evolve and the situation presents new logistical challenges. Some clinical and epidemiological values may thus be refined as additional meta-analyses become available. The logistics of contact tracing at the unprecedented scale of over 200, 000 daily cases may prevent the identification of all contacts, thus lowering what was used as a practically feas...

    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 10.1101/2020.12.11.20247825v1 on medRxiv