Model: IQVIA ACOE - IQVIA_ACOE-STAN

    Name: STAN
    Abbreviation: IQVIA_ACOE-STAN
    Owner: covid19hub
    Team name: IQVIA ACOE
    Description: Spatio-Temporal Attention Network
    Contributors: Junyi Gao (IQVIA), Rakshith Sharma (Georgia Institute of Technology), Cheng Qian (IQVIA) <alextoqc@gmail.com>, Lucas M. Glass (IQVIA), Jeffrey Spaeder (IQVIA), Justin Romberg (Georgia Institute of Technology), Jimeng Sun (University of Illinois, Urbana-Champaign), Cao Xiao (IQVIA)
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: https://arxiv.org/abs/2008.04215
    Methods: We proposed a spatio-temporal attention network (STAN) for state-level pandemic prediction. Our method aims at addressing these limitations and making earlier and more accurate pandemic outbreak predictions by (1) using patients EHR data from different 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. STAN 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.
    Home: https://www.iqvia.com/landing/analytics-center-of-excellence
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