Virtual Patient Cohorts to Illuminate Immunologic Drivers of Influenza Severity

Project Summary Influenza viruses result in a significant number of illnesses and deaths each year highlighting its health and economic burden. Management of this disease is difficult, and little is known about how different host factor contribute to heterogenous outcomes. To advance the goals of understanding the diverse immune responses to influenza and predict risk, it is essential to develop new tools that can define individualized immune trajectories, simultaneously account for multiple sources of heterogeneity, and accurately predict dynamics that drive disease progression. This project addresses gaps in identifying the impact that host factors have disease outcome and gaps in developing computational methods for respiratory infections that accurately predict inflammation and disease severity. The studies will develop and exploit new predictive systemic immune models and simulate human populations using virtual patient cohorts aims at differentiating clinical outcomes and identify downstream effects of varying levels of basal immunity.