  Accurately forecasting county level COVID-19 confirmed cases is crucial to
optimizing medical resources. Forecasting emerging outbreaks pose a particular
challenge because many existing forecasting techniques learn from historical
seasons trends. Recurrent neural networks (RNNs) with LSTM-based cells are a
logical choice of model due to their ability to learn temporal dynamics. In
this paper, we adapt the state and county level influenza model, TDEFSI-LONLY,
proposed in Wang et a. [l2020] to national and county level COVID-19 data. We
show that this model poorly forecasts the current pandemic. We analyze the two
week ahead forecasting capabilities of the TDEFSI-LONLY model with combinations
of regularization techniques. Effective training of the TDEFSI-LONLY model
requires data augmentation, to overcome this challenge we utilize an SEIR model
and present an inter-county mixing extension to this model to simulate
sufficient training data. Further, we propose an alternate forecast model, {\it
County Level Epidemiological Inference Recurrent Network} (\alg{}) that trains
an LSTM backbone on national confirmed cases to learn a low dimensional time
pattern and utilizes a time distributed dense layer to learn individual county
confirmed case changes each day for a two weeks forecast. We show that the
best, worst, and median state forecasts made using CLEIR-Net model are
respectively New York, South Carolina, and Montana.
