  Objective: The COVID-19 pandemic has created many challenges that need
immediate attention. Various epidemiological and deep learning models have been
developed to predict the COVID-19 outbreak, but all have limitations that
affect the accuracy and robustness of the predictions. Our method aims at
addressing these limitations and making earlier and more accurate pandemic
outbreak predictions by (1) using patients' EHR data from different counties
and states that encode local disease status and medical resource utilization
condition; (2) considering demographic similarity and geographical proximity
between locations; and (3) integrating pandemic transmission dynamics into deep
learning models. Materials and Methods: We proposed a spatio-temporal attention
network (STAN) for pandemic prediction. It uses an attention-based graph
convolutional network to capture geographical and temporal trends and predict
the number of cases for a fixed number of days into the future. We also
designed a physical law-based loss term for enhancing long-term prediction.
STAN was tested using both massive real-world patient data and open source
COVID-19 statistics provided by Johns Hopkins university across all U.S.
counties. Results: STAN outperforms epidemiological modeling methods such as
SIR and SEIR and deep learning models on both long-term and short-term
predictions, achieving up to 87% lower mean squared error compared to the best
baseline prediction model. Conclusions: By using information from real-world
patient data and geographical data, STAN can better capture the disease status
and medical resource utilization information and thus provides more accurate
pandemic modeling. With pandemic transmission law based regularization, STAN
also achieves good long-term prediction performance.
