Mask mandates can limit COVID spread: Quantitative assessment of month-over-month effectiveness of governmental policies in reducing the number of new COVID-19 cases in 37 US States and the District of Columbia
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Abstract
Introduction
SARS-CoV-2 is the beta-coronavirus responsible for COVID-19. Facemask use has been qualitatively associated with reduced COVID-19 cases, but no study has quantitatively assessed the impact of government mask mandates ( MM ) on new COVID-19 cases across multiple US States.
Data and Methods
We utilized a non-parametric machine-learning algorithm to test the a priori hypothesis that MM were associated with reductions in new COVID-19 cases. Publicly available data were used to analyze new COVID-19 cases from 37 States and the District of Columbia (i.e., “38 States”). We conducted confirmatory All-States and State-Wise analyses, validity analyses [e.g., leave-one-out (LOO) and bootstrap resampling], and covariate analyses.
Results
No statistically significant difference in the daily number of new COVID-19 infections was discernable in the All-States analysis. In State-Wise LOO validity analysis, 11 States exhibited reductions in new COVID-19 and the reductions in four of these States (AK, MA, MN, VA) were significant in bootstrap resampling. Only the Social Capital Index predicted MM success (training p <0.028 and LOO p <0.013).
Conclusion
Results obtained when studying the impact of MM on COVID-19 cases varies as a function of the heterogeneity of the sample being considered, providing clear evidence of Simpson’s Paradox and thus of confounded findings. As such, studies of MM effectiveness should be conducted on disaggregated data. Since transmissions occur at the individual rather than at the collective level, additional work is needed to identify optimal social, psychological, environmental, and educational factors which will reduce the spread of SARS-CoV-2 and facilitate MM effectiveness across diverse settings.
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SciScore for 10.1101/2020.10.06.20208033: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Exact discrete 95% confidence intervals (CI) for the given ODA model (“Model”) and for randomly scrambled observations from the model (“Chance”) were obtained. Blinding not detected. Power Analysis Third, to maximize the available statistical power of our analyses for estimating short-term effects (see Statistical Analysis), we extracted the number of cases for up to 30 days before and up to 31 days after the MM was implemented (yielding a maximum of n=61 data points for each state). Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources 46 Graphical depictions of new cases organized across … SciScore for 10.1101/2020.10.06.20208033: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Exact discrete 95% confidence intervals (CI) for the given ODA model (“Model”) and for randomly scrambled observations from the model (“Chance”) were obtained. Blinding not detected. Power Analysis Third, to maximize the available statistical power of our analyses for estimating short-term effects (see Statistical Analysis), we extracted the number of cases for up to 30 days before and up to 31 days after the MM was implemented (yielding a maximum of n=61 data points for each state). Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources 46 Graphical depictions of new cases organized across days, and histograms of bootstrapped 95% CIs, were created using ggplot2 for R. ggplot2suggested: (ggplot2, RRID:SCR_014601)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:However, this finding has limitations. States with higher SCI should not be interpreted as being inherently better at reducing new SARS-CoV-2 cases, but rather we believe this finding also supports a new corollary hypothesis that States with lower SCI may require different and multifaceted interventions to address the pandemic.
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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