Model: University of Chicago, Chattopadhyay Lab - UChicagoCHATTOPADHYAY-UnIT

    Name: Universal Influenza-like Transmission Risk
    Abbreviation: UChicagoCHATTOPADHYAY-UnIT
    Owner: covid19hub
    Team name: University of Chicago, Chattopadhyay Lab
    Description: Stochastic similarity via sequence likelihood divergence, General Linear Models, Tensorflow, Ensemble Regressors
    Contributors: Yi Huang (University of Chicago), Ishanu Chattopadhyay (University of Chicago)<ishanu@uchicago.edu>
    License: MIT
    Notes: primary
    Citation: Missing
    Methods: We introduce the concept of a universal geo-spatial risk that strongly influences person-to-person viral transmission in the US, in effect quantifying the risk exposure of the individual US counties to diseases thattransmit like the seasonal Influenza. We call this the Universal Influenza-like Transmission (UnIT) score. This new epidemiologically informed risk score leverages subtle geospatial patterns from seasonal flu epidemics of the past, and significantly improves incidence forecasts for COVID-19. Additionally, the UnIT score emerges as the most important factor explaining observed county-specific incidence trends for COVID-19 in the US, with coefficients in multi-variate regression far outweighing those for typical co-variates. Thus, our key insight is that past incidence patterns for a different pathogen can substantially inform current projections, provided the transmission mechanisms are broadly similar. And our key technical contribution is the development of the precise algorithm to transfer this knowledge to the new context, by rigorously estimating thedivergence of hidden generators of complex stochastic phenomena from observation of short sample paths.
    Home: https://github.com/zeroknowledgediscovery/unitcov
    Auxiliary data: Missing

    Forecasts (15)

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