Estimating the Effect of Social Distancing Interventions on COVID-19 in the United States

  1. Andrew M Olney
  2. Jesse Smith
  3. Saunak Sen
  4. Fridtjof Thomas
  5. H Juliette T Unwin

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Since its global emergence in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused multiple epidemics in the United States. When medical treatments for the virus were still emerging and a vaccine was not yet available, state and local governments sought to limit its spread by enacting various social-distancing interventions, such as school closures and lockdowns; however, the effectiveness of these interventions was unknown. We applied an established, semimechanistic Bayesian hierarchical model of these interventions to the spread of SARS-CoV-2 from Europe to the United States, using case fatalities from February 29, 2020, up to April 25, 2020, when some states began reversing their interventions. We estimated the effects of interventions across all states, contrasted the estimated reproduction numbers before and after lockdown for each state, and contrasted the predicted number of future fatalities with the actual number of fatalities as a check of the model’s validity. Overall, school closures and lockdowns were the only interventions modeled that had a reliable impact on the time-varying reproduction number, and lockdown appears to have played a key role in reducing that number to below 1.0. We conclude that reversal of lockdown without implementation of additional, equally effective interventions will enable continued, sustained transmission of SARS-CoV-2 in the United States.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.10.20151001: (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:
    Our study has several limitations. First, the assumption that all interventions have the same implementation and effect in all states is a strong assumption. For example, states with a stronger culture of recreational sports will likely see a greater impact of the sports intervention than states without that culture. More directly, the intervention banning public gatherings of 100 persons or more could be met by a ban on 10 persons or more or 50 persons or more; it is unlikely that such bans are truly equivalent. This limitation has since been partially addressed in the European model by allowing random effects for lockdown only. Second, the assumption that interventions are binary rather than continuously varying is also a strong assumption and clearly an oversimplification, because it does not account for time-varying compliance with intervention. Several groups are incorporating mobility data as a measure of population mixing (Unwin et al., 2020; Woody et al., 2020; Team and Murray, 2020). Third, the parameters of the model are estimated using reasonable, but still uncertain, assumptions about prior distributions. We have used the same assumptions as in the European model, but these assumptions may be contradicted by future empirical work. Modeling of SARS-CoV-2 is emerging and rapidly diversifying, including classical SEIR models and derivatives (Pei and Shaman, 2020), deep learning (Prakash, 2020), and piecewise models for sub-exponential growth (Scire et al., 2020). Sta...

    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.

    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 10.1093/aje/kwaa293
  3. Version published to 10.1101/2020.07.10.20151001v1 on medRxiv