Quantifying the impact of US state non-pharmaceutical interventions on COVID-19 transmission

  1. Hannah M. Korevaar
  2. Alexander D. Becker
  3. Ian F. Miller
  4. Bryan T. Grenfell
  5. C. Jessica E. Metcalf
  6. Michael J. Mina

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Abstract

COVID-19 is an ongoing public health emergency. Without a vaccine or effective antivirals, non-pharmaceutical interventions form the foundation of current response efforts. Quantifying the efficacy of these interventions is crucial. Using mortality data and a classification guide of state level responses, we relate the intensity of interventions to statistical estimates of transmission, finding that more stringent control measures are associated with larger reductions in disease proliferation. Additionally, we observe that transmission increases with population density, but not population size. These results may help inform future response efforts.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.30.20142877: (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
    To estimate time-varying transmission rates, we used the EpiEstim R package [16, 12].
    EpiEstim
    suggested: (EpiEstim, RRID:SCR_018538)

    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:
    Deaths as a lagged proxy for incidence should be less affected by the complicated reality of testing, though subject to its own set of caveats (e.g. high transmissibility in nursing homes may artificially inflate Re estimates). Given all these complexities, our estimates of Re and the magnitude of reductions may not be precise; even so, we believe the estimated reductions in Re are qualitatively robust. In other words, we believe the trends we observe in our estimates are real even if the estimates are not exact. Research using more sophisticated and exact theoretical methods is ongoing and likely to provide deeper, and more exact, estimates of transmission [8]. Finally, confounding, and unanalyzed factors, such as underlying health conditions and / or under-reporting were not considered in our analysis and may bias our estimates at the county-level. Heterogeneities at the state and county levels not included in our analysis such as age structure, contact structure, policy reinforcement, and policy adherence may explain some of the variability in the estimated impact of each state action. Importantly, while these results add evidence to the effectiveness of NPIs in curbing transmission, they can only be interpreted in the context of interventions to date. Most crucially, these results do not provide a pathway to reopening business, counties, or states. The reduction in transmission associated with moving from no to high intervention, for example, does not necessarily imply an...

    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

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  2. Version published to 10.1101/2020.06.30.20142877v1 on medRxiv