Modeling Core - Drivers of influenza A virus transmission in humans

Emory University, Atlanta GA

Investigators

Abstract

SUMMARY - Modeling Core Modeling is a key tool for dissecting the respective roles that interacting processes play in regulating viral and immunological dynamics. Models are most useful when they are statistically interfaced with data, as such an interface allows for model parameterization and evaluation. The Modeling Core of this P01 Program will provide modeling expertise and resources to Projects 1 and 2 to uncover the critical interactions between host immune responses and influenza virus populations that underpin the process of transmission. The Modeling Core includes established researchers with expertise in modeling within-host and between-host viral dynamics in the context of the host immune response. The Modeling Core will leverage the combined expertise of our team to identify viral, immunological, and host features driving within-host viral dynamics, the expulsion of infectious virus, and onward transmission success. The identification of these processes and the extent to which they regulate within-host and between-host viral dynamics requires rigorous quantitative analyses along with mathematical modeling. We will organize these efforts within two Specific Aims. The first Aim will develop models that examine viral and immunological dynamics within hosts. We will develop and apply models for the dynamics of virus populations, innate immunity, and adaptive immunity during infection to identify processes regulating within-host viral dynamics. We will also develop and apply within-host viral evolutionary models to identify processes regulating patterns of within-host viral diversity and adaptation. The second Aim will focus on between- host processes. It will develop models to identify host physiological and immunological factors affecting virus acquisition and transmission. These include statistical models to understand patterns of viral expulsion into the air from infected participants and models to improve understanding of immunological and other factors that determine infection outcomes in challenged participants. Finally, we will apply mathematical models to characterize virus genetic bottlenecks acting on viral populations during transmission. Overall, through the integration of diverse datasets using statistical and mechanistic modeling, we will advance understanding of the dynamic interactions between virus and host that define the potential for onward transmission.