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A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties (preprint)
researchsquare; 2021.
Preprint
in English
| PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-456641.v1
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.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-RESEARCHSQUARE
Main subject:
COVID-19
Language:
English
Year:
2021
Document Type:
Preprint
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