team_name: University of Southern California
model_name: SIkJalpha
model_abbr: USC-SIkJalpha
model_contributors:
  - name: Ajitesh Srivastava
    email: ajiteshs@usc.edu
  - name: Frost Tianjian Xu
website_url: https://scc-usc.github.io/ReCOVER-COVID-19
license: mit
team_model_designation: primary
methods: 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.
team_funding: Supported by US National Science Foundation Award No. 2027007 (RAPID) [May 2020 - April 2021]
repo_url: https://github.com/scc-usc/ReCOVER-COVID-19
data_inputs: JHU (cases, deaths), Our World in Data (vaccinations)
citation: https://arxiv.org/abs/2007.05180
methods_long: 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.