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County-Specific, Real-Time Projection of the Effect of Business Closures on the COVID-19 Pandemic

Yurk, Dominic and Abu-Mostafa, Yaser (2021) County-Specific, Real-Time Projection of the Effect of Business Closures on the COVID-19 Pandemic. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210212-100829452

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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 Rt 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 (r2=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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.02.10.21251533DOIDiscussion Paper
https://www.medrxiv.org/content/10.1101/2021.02.10.21251533v1OrganizationDiscussion Paper
Additional Information:The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license. This version posted February 12, 2021. We thank Dr. Jeremy Goldhaber-Fiebert for providing us with early access to the SC-COSMO public health policy data set, which helped kick-start this research effort. Our overall COVID modeling research effort was supported by the Clinard Innovation Fund. Author Declarations: I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes. The details of the IRB/oversight body that provided approval or exemption for the research described are given below: None. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes. I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes. I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes. Data Availability: All data used are available through links referenced in the manuscript. Competing Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: financial support from the Clinard Innovation Fund, which has no conflict of interest with this work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.
Group:COVID-19
Funders:
Funding AgencyGrant Number
Clinard Innovation FundUNSPECIFIED
Subject Keywords:data-driven; model; COVID-19; policy; neural-net
Record Number:CaltechAUTHORS:20210212-100829452
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210212-100829452
Official Citation:County-Specific, Real-Time Projection of the Effect of Business Closures on the COVID-19 Pandemic. Dominic Yurk, Yaser Abu-Mostafa. medRxiv 2021.02.10.21251533; doi: https://doi.org/10.1101/2021.02.10.21251533
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108034
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Feb 2021 18:15
Last Modified:12 Feb 2021 18:15

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