Which COVID policies are most effective? A Bayesian analysis of COVID-19 by jurisdiction
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Abstract
This paper reports the results of a Bayesian analysis on large-scale empirical data to assess the effectiveness of eleven types of COVID-control policies that have been implemented at various levels of intensity in 40 countries and U.S. states since the onset of the pandemic. The analysis estimates the marginal impact of each type and level of policy as implemented in concert with other policies. The purpose is to provide policymakers and the general public with an estimate of the relative effectiveness of various COVID-control strategies. We find that a set of widely implemented core policies reduces the spread of virus but not by enough to contain the pandemic except in a few highly compliant jurisdictions. The core policies include the cancellation of public events, restriction of gatherings to fewer than 100 people, recommendation to stay at home, recommended restrictions on internal movement, implementation of a partial international travel ban, and coordination of information campaigns. For the median jurisdiction, these policies reduce growth rate in new infections from an estimated 270% per week to approximately 49% per week, but this impact is insufficient to prevent eventual transmission throughout the population because containment occurs only when a jurisdiction reduces growth in COVID infection to below zero. Most jurisdictions must also implement additional policies, each of which has the potential to reduce weekly COVID growth rate by 10 percentage points or more. The slate of these additional high-impact policies includes targeted or full workplace closings for all but essential workers, stay-at-home requirements, and targeted school closures.
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SciScore for 10.1101/2020.12.01.20241695: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:As with any study, these results must be interpreted within the limitations of the methods used. One limitation is that we need to make several assumptions as described in the Methods section about disease epidemiology and the impact of government policy. In the Supporting Information, we have included several robustness checks that demonstrate that our results are relatively insensitive within reasonable parameters to specific …
SciScore for 10.1101/2020.12.01.20241695: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:As with any study, these results must be interpreted within the limitations of the methods used. One limitation is that we need to make several assumptions as described in the Methods section about disease epidemiology and the impact of government policy. In the Supporting Information, we have included several robustness checks that demonstrate that our results are relatively insensitive within reasonable parameters to specific assumptions. Second, because this is an observational study, it is inherently limited because the findings may be biased by variables that are not included in the study. For instance, the relatively limited impact of testing (H2) and contact tracing (H3) in Figure 2 could arise from the lifting of policies others than those reported in the Oxford database around the same time that H2 and H3 policies were implemented. While we strive to limit the impact of omitted-variable bias through reliance on 23 different policy levels, the findings must be interpreted with caution. Overall, the results should be interpreted with these limitations in mind and should be relied on only in conjunction with results from other studies, including computational models and controlled experiments such as those described in our review of the literature.
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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