Model: SUNY Upstate and SU Covid-19 Prediction Team - UpstateSU-GRU

    Name: GRU
    Abbreviation: UpstateSU-GRU
    Owner: covid19hub
    Team name: SUNY Upstate and SU Covid-19 Prediction Team
    Description: County-level forecast using recurrent neural network seq2seq model with the Gated recurrent units (GRU)
    Contributors: Yanli Zhang-James (SUNY Upstate Medical University) <zhangy@upstate.edu>, Asif Salekin (Syracuse University) <asalekin@syr.edu>, Jonathan Hess (SUNY Upstate Medical University) <hessjlmsg@gmail.com>, Samuel Chen (SUNY Upstate Medical University) <ChenSa@upstate.edu>, Stephen V Faraone (SUNY Upstate Medical University) <sfaraone@childpsychresearch.org>
    License: MIT
    Notes: primary
    Citation: Missing
    Methods: We adopted the Sequence-to-sequence learning framework (Seq2Seq) that powers the language-processing apps like Google Translate (Sutskever et al., 2014). The Seq2Seq model is a feed-forward recurrent neural networks (RNN) that are specialized in mapping sequences. The Seq2Seq algorithm trains a model to convert sequences from the input to sequences in the output. Our model input sequences include daily smoothed incident cases and deaths counts google mobility index, and daily reproduction number, R (calculated based on daily indecent cases). We also aggregate the county demographic and health risk indices to model the baseline risk score. The model takes ‘m’ days data (as well as the demographic and health risk indices) as input, and predict covid-19 cases or deaths for ‘n’ future days.
    Home: ylzhang29.github.io/UpstateSU-GRU-Covid
    Auxiliary data: https://github.com/ylzhang29/UpstateSU-GRU-Covid

    Forecasts (3)

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    2021-01-18 2021-01-18-UpstateSU-GRU.csv 2021-01-19 01:26:27 UTC 2021-01-18
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    2021-01-11 2021-01-11-UpstateSU-GRU.csv 2021-01-12 16:56:21 UTC 2021-01-12
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    2021-01-04 2021-01-04-UpstateSU-GRU.csv 2021-01-05 06:36:50 UTC 2021-01-05
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