County-Specific, Real-Time Projection of the Effect of Business Closures on the COVID-19 Pandemic
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Abstract
Public health policies such as business closures have been one of our most effective tools in slowing the spread of COVID-19, but they also impose costs. This has created demand from policy makers for models which can predict when and where such policies will be most effective to head off a surge and where they could safely be loosened. No current model combines data-driven, real-time policy effect predictions with county-level granularity. We present a neural net-based model for predicting the effect of business closures or re-openings on the COVID-19 time-varying reproduction number R t in real time for every county in California. When trained on data from May through September the model accurately captured relative county dynamics during the October/November California COVID-19 surge ( r 2 = 0.76), indicating robust out-of-sample performance. To showcase the model’s potential utility we present a case study of various counties in mid-October. Even when counties imposed similar restrictions at the time, our model successfully distinguished counties in need of drastic and immediate action to head off a surge from counties in less dire need of intervention. While this study focuses on business closures in California, the presented model architecture could be applied to other policies around world.
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SciScore for 10.1101/2021.02.10.21251533: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2021.02.10.21251533: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
Results from scite Reference Check: We found no unreliable references.
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