Multi-Scale Investigation of SARS-CoV-2 Infection

By the beginning of May 2021, novel coronavirus-2 severe acute respiratory syndrome (SARS-CoV-2) has infected more than 152 million people and resulted in more than 3.1 million deaths worldwide. This has caused an urgent need for the development of novel antivirals and vaccines capable of detecting, preventing, and treating the infection, and to date, more than a billion people have received at least one vaccine dose. While the pandemic is unfolding, guidance regarding testing and quarantining strategies in vaccinated and unvaccinated populations is needed in order to control its effects. This project uses a multi-disciplinary approach, merging virological and immunological data with mathematical and computational models to uncover causes for variability in individual infections, their contributions to transmission in the population, markers of severe infections, and best testing and quarantining strategies in the presence and absence of vaccination. These approaches will provide testable hypotheses for targeting specific proteins and types of treatments for severe infections with SARS-CoV-2. Suggesting SARS-CoV-2 mitigation strategies is complicated by limited knowledge regarding SARS-CoV-2 dynamics inside an individual, the immune responses it elicits, and the uncertainty of whether infections result in no, partial, or life-long sterilizing immunity. The focus of this project is to use novel multi-scale mathematical models at the molecular, cellular and population levels with the aim of predicting the kinetics of SARS-CoV-2 inside an individual, the mechanisms of immune protection and those that lead to pathogenesis, and the connection between individual infections, testing strategies, and the transmission of the virus into the population. The research will focus on three major mathematical challenges: (1) Developing within-host between-host multi-scale systems; (2) Developing data analytics methods for model parameter and uncertainty estimation; and (3) Establishing an optimal control problem for testing practices. The overarching goal of the project is to integrate mathematical models with single-cell neutrophil data, SARS-CoV-2 virus titers, and knowledge of testing abilities to better control the spread of SARS-Cov-2 and reduce disease severity. By bridging the scales, the PI aims to provide a unified picture of SAS-CoV-2 infection that can benefit both the medical and the public health community. 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.