RAPID: Retrospective COVID-19 Scenario Projections Accounting for Population Heterogeneities

The long-term burden of COVID-19 may vary across races and ethnicities. To address this variaiton this project will extend a current model to account for race and ethnicity. The availability of outcomes and vaccine uptake data by race/ethnicity in the US creates an opportunity to explicitly model these variables across the groups and evaluate the results from real-world data. The project will help us understand the inequities of COVID-19 outcomes and vaccination uptake and prepare the US for the future of COVID-19 and other outbreaks. The project has the potential to be applicable wherever relevant data on ethnicity and race is available, and can be extended to other types of groups. The project will integrate the lessons learned in an undergraduate course on programming and a graduate-level class on Machine Learning for health. The project will also provide research opportunities through a senior capstone program and minority-serving programs such as the USC JumpStart program and the Viterbi Summer Institute. The proposed project will integrate data on race and ethnicity along with various other datasets to account for population health. The key innovation in the integration is the ability to learn contact matrices from data. The project will use a novel approach, where the n×n contact matrix is generated by n hidden parameters that indicate the likelihood of contact of a group with a randomly selected individual. The learned contact matrix will be integrated with an epidemiological model currently being used by the PI in the US Scenario Modeling Hub to generate long-term projections of cases, deaths, and hospitalization. The appoach will compare learning contact matrices with other approaches that derive those matrices from survey data and high-resolution mobility data. The new approach will enable the modeling of sub-population interactions when such mobility data is not available. The model will be evaluated with ground truth data observed over the last three years in collaboration with the COVID-19 Scenario Modeling Hub. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.