Modeling across-scale feedbacks of infectious diseases

A current challenge for disease modeling and public health is to understand pathogen dynamics across infection scales from within-host to between-host. Viral and immune response kinetics upon infection impact transmission to other hosts and feedback into population-wide immunity, all of which influence the severity, trajectory, and evolution of a spreading pathogen. In this talk, I will introduce structured partial differential equation models linking immunology and epidemiology in order to investigate coevolution of virus and host, multi-scale data fitting, and impacts of dynamic host immunity from an individual to the whole population. We apply the models to vector-borne diseases, such as Rift Valley fever (RVF) and dengue (DENV), with immunological and epidemiological data. Using invasion dynamics analysis and multi-scale numerical methods, we characterize different scenarios of virus-host evolution and coexistence of viral strains under waning host cross-immunity. In the case of DENV, we recapitulate how intermediate levels of pre-existent antibodies enhance infection within a host, and how to scale up to distributions of antibody levels among epidemiological classes in the host population to determine risk of severe DENV prevalence. These results have implications for optimal vaccination policy, and the modeling framework developed here is currently being applied to examine the emergence of COVID-19 variants partially resistant to antibodies induced by host infection or vaccination.