Show simple item record

dc.creatorGao, Junyi
dc.creatorSharma, Rakshith
dc.creatorQian, Cheng
dc.creatorGlass, Lucas M.
dc.creatorSpaeder, Jeffrey
dc.creatorRomberg, Justin
dc.creatorSun, Jimeng
dc.creatorXiao, Cao
dc.identifier.citationJunyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, Cao Xiao, STAN: spatio-temporal attention network for pandemic prediction using real-world evidence, Journal of the American Medical Informatics Association, 2021;, ocaa322,
dc.description.abstractOBJECTIVE: 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.
dc.format.extent11 pages
dc.relation.ispartofCOVID-19 Research
dc.relation.haspartJournal of the American Medical Informatics Association
dc.relation.isreferencedbyOxford University Press
dc.rightsAttribution CC BY
dc.subjectPandemic prediction
dc.subjectDeep learning
dc.subjectGraph attention network
dc.subjectReal world evidence
dc.titleSTAN: spatio-temporal attention network for pandemic prediction using real-world evidence
dc.type.genreJournal article
dc.description.departmentBusiness and Management
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact
dc.description.schoolcollegeFox School of Business
dc.temple.creatorGlass, Lucas M.

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution CC BY
Except where otherwise noted, this item's license is described as Attribution CC BY