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A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties
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2021-04-20
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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 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.