STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence

Understand

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.