Model: University of Southern California Data Science Lab - USC-SI_kJalpha

    Name: SI_kJalpha
    Abbreviation: USC-SI_kJalpha
    Owner: covid19hub
    Team name: University of Southern California Data Science Lab
    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>, Viktor K. Prasanna <prasanna@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 101.03s. Despite being fast, the accuracy of our forecasts is on par with the state-of-the-art as demonstrated on the leaderboard page. at https://scc-usc.github.io/ReCOVER-COVID-19. 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. For each state, we model hospitalizations as a separate compartment, as a linear function of recent cases with heterogeneous rates. This means that for some hyper-parameter J, those who were infected at time t-1 to t-J will have a separate rate from those infected at t-(J+1) to t-2J, and so on. This is similar to how we perform death forecasts. For long-term forecasts (more than a few days in the future), the cases are forecasted first based on our SIkJalpha model, which forms the input to hospitalization prediction. While we account for changing trends by putting more emphasis on recently seen data, we assume that the trends will remain the same in the future for our point forecasts. We try to account for changing trends in the future in our quantile forecasts by modeling the empirical error our approach makes using a Random Forest.
    Home: https://scc-usc.github.io/ReCOVER-COVID-19
    Auxiliary data: https://github.com/scc-usc/ReCOVER-COVID-19

    Forecasts (60)

    Timezero Data Source Upload Time Issue date Version
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    2021-04-04 2021-04-04-USC-SI_kJalpha.csv 2021-04-05 06:20:03 UTC 2021-04-05
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