A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

ABSTRACT: The 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 m

SUBMITTER: Zhang-James Y 

PROVIDER: S-EPMC8132245 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature