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dc.contributor.authorZhang-James, Yanli
dc.contributor.authorHess, Jonathan
dc.contributor.authorSalekin, Asif
dc.contributor.authorWang, Dongliang
dc.contributor.authorChen, Samuel
dc.contributor.authorWinkelstein, Peter
dc.contributor.authorMorley, Christopher P
dc.contributor.authorFaraone, Stephen V.
dc.date.accessioned2021-07-06T20:08:25Z
dc.date.available2021-07-06T20:08:25Z
dc.date.issued2021-04-20
dc.identifier.doi10.1101/2021.04.14.21255507
dc.identifier.urihttp://hdl.handle.net/20.500.12648/1814
dc.description.abstractThe global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.en_US
dc.publisherCold Spring Harbor Laboratoryen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCovid-19, Deep Learning, Machine Learning, Gated Recurrent Unit, Pandemic Forecasting, epidemic transmissionen_US
dc.titleA seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US countiesen_US
dc.typeArticleen_US
dc.description.versionSMURen_US
refterms.dateFOA2021-07-06T20:08:26Z
dc.description.institutionUpstate Medical Universityen_US
dc.description.departmentPsychiatryen_US
dc.description.degreelevelN/Aen_US


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International