Mathematical Biology Seminar: Using Mathematical Models to Predict Vaccine Strategies for Viral Infections

Mathematical models of infectious disease dynamics have helped to advance our basic understanding of the epidemiology and pathogenesis of some diseases. Models have been used to predict the impact of prevention efforts or to assess host-pathogen mechanisms. Efforts are currently underway to develop both pre-exposure and post-exposure vaccines for several viral infections, including Human Immunodeficiency Virus type 1 (HIV-1) and Herpes Simplex Virus type 2 (HSV-2). In this talk, I will present models of vaccination strategies for these viral infections. Results using deterministic models of the HSV-2 epidemic showed that imperfect vaccines could reduce new infections, but vaccines providing therapeutic benefits that do not lower transmission are likely to have little impact on epidemic control. For HIV-1 infection, I will show a stochastic model of viral mutation and the immune response that reproduces phenomena seen in clinical data; such a model can be used to predict conditions under which a vaccine would be most effective. These studies are potentially useful to guide future strategies for the development of vaccines and other preventative or therapeutic interventions.