Objective We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients’ claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model. Materials and Methods We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties. Results STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model. Conclusions By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization.

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JNC7-concordance is high overall, but drops substantially when JNC7 recommendations are more demanding (e.g., among patients with stage 2 hypertension and/or CKD, CHF, diabetes). Overall, patients who are prescribed an antihypertensive regimen that is JNC7-concordant are more likely to achieve BP control.