Collaborative Research: Multiscale modeling of viral infections: Dynamics and applications

Viral infections grow and spread through processes that occur at many connected levels, from events inside infected cells, to the body’s defense against infection, and to the spread of disease across communities. However, these processes are often studied separately, making it difficult to understand how changes at one level affect outcomes at another. This project will develop new mathematical and computational tools to connect these levels in one framework. The work will help researchers better understand how viruses grow, how the body responds to infection, how treatments and prevention measures work, and how infections spread in populations. By linking these processes together, the project may provide useful guidance for evaluating strategies to reduce the impact of viral diseases. Educational and outreach activities will provide interdisciplinary training opportunities and introduce students to the application of mathematics in biomedical research and public health. This project will develop and analyze a multiscale mathematical modeling framework for viral infections by integrating intracellular and extracellular dynamics within the human body with disease transmission between host populations. At the intracellular level, the models will describe viral entry, replication, assembly, and release. At the extracellular level, the models will describe viral kinetics and immune responses, including antibody and cellular immune responses. At the population level, the models will incorporate multiple transmission routes, symptomatic and asymptomatic infection, vaccination, waning immunity, and variant emergence. These components will be coupled into unified multiscale systems to study how molecular mechanisms, host immune responses, and transmission processes interact across scales. The project will combine dynamical systems analysis, bifurcation theory, stochastic modeling, data fitting, sensitivity analysis, and numerical simulation. The resulting models will provide a mathematical basis for studying treatment effects, vaccine impacts, variant dynamics, and epidemic outcomes across multiple biological scales. 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. NSF Award ID: 2602110 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Jin Wang | Institution: University of Tennessee Chattanooga, CHATTANOOGA, TN | Award Amount: $173,777 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2602110 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2602110.html