RAPID: Data-driven Understanding of Imperfect Protection for Long-term COVID-19 Projections

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This project will use data on COVID-19 reinfections and vaccine breakthroughs to build a model of how imperfect immunity affects SARS-CoV-2 pathogen transmission dynamics and subsequent effects on numbers of cases, deaths, and hospitalizations. A key factor dictating the long-term dynamics of COVID-19 is how population immunity against COVID-19 changes over time and exposure. Data on vaccination breakthroughs and reinfections in various states of the US and countries around the world create a unique opportunity to study immunity waning dynamics at the population level. The project will help understand the long-term risks of resurgence and severity of COVID-19, contribute to the US COVID-19 Scenario Modeling Hub, the US COVID-19 Forecast Hub, and the European COVID-19 Forecast and Scenario Modeling Hubs, and thus inform policymakers worldwide. The PI will integrate the lessons learned in an undergraduate on programming and a graduate-level class on Machine Learning for health. The project will also provide research and training opportunities through a senior capstone program and a minority-serving program. The model will represent a class of imperfect protection in the presence of multiple variants. Popular models such as all or nothing, leaky, and time-dependent waning will be considered along with interpretable machine learning models. The models will be validated by their “generalizability” on held-out data. The unified model of immunity will be developed in a way that it can be integrated with various epidemiological models. As a demonstration, it will be integrated with a model that tracks various states an individual can be in, including all permutations of infections, reinfections, one-dose, two-doses, and boosters. Having these states over time, age groups, and variants, for a given model of imperfect protection allows for precise computation of immunity in the population at a given time. The overall approach will also be evaluated by the accuracy of US state-level cases, deaths, and hospitalization forecasts it produces. This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.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.