Model: University of Southern California - USC-SIkJalpha

    Name: SIkJalpha
    Abbreviation: USC-SIkJalpha
    Owner: epiforecasts
    Team name: University of Southern California
    Description: A heterogeneous infection rate model with human mobility for epidemic modeling. Our model adapts to changing trends and provide predictions of confirmed cases and deaths.
    Contributors: Ajitesh Srivastava <ajiteshs@usc.edu>, Frost Tianjian Xu
    License: MIT
    Notes: primary
    Citation: https://arxiv.org/abs/2007.05180
    Methods: We use our own epidemic model called SI-kJalpha, preliminary version of which we have successfully used during DARPA Grand Challenge 2014. Our model can consider the effect of many complexities of the epidemic process and yet be simplified to a few parameters that are learned using fast linear regressions. Therefore, our approach can learn and generate forecasts extremely quickly. On a 2 core desktop machine, our approach takes only 3.18s to tune hyper-parameters, learn parameters and generate 100 days of forecasts of reported cases and deaths for all the states in the US. The total execution time for 184 countries is 11.83s and for more than 3000 US counties is 30s. Despite being fast, the accuracy of our forecasts is on par with the state-of-the-art as demonstrated by our evaluation and benchmarking page at https://scc-usc.github.io/covid19-forecast-bench Our model is able to quickly adapt to changing trends, and the variations in parameters during different times/policies allow us to forecast different scenarios such as what would happen if we were to disregard social distancing suggestions.
    Home: https://scc-usc.github.io/ReCOVER-COVID-19
    Auxiliary data: https://github.com/scc-usc/ReCOVER-COVID-19

    Forecasts (83)

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