Optimizing Vaccination Strategies within a Population-Pathogen Landscape

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Modelling and computational approaches provide powerful tools in the study of disease dynamics at both the individual and population levels. Recent analytical developments, coupled with advances in information and communications technologies, have opened up novel vistas and presented new challenges in mathematical immunology and epidemiology. Mathematical, statistical, and computational modelling has enormous potential to address these challenges, offering integrated frameworks for making assumptions explicit and for best using limited data, highlighting key factors that determine policy needs, providing quantitative projections, and assessing risks, benefits, and trade-offs that are implicit in disease dynamics and control programs. The utility of mathematical and computational modelling is well-documented. An important example is the implementation of model-based recommendations for vaccination strategies and use of antiviral drugs during the 2009 pandemic influenza. By developing and applying these models, this proposal aims to address the following objectives:1. Evaluate the effect of population demographics on the outcome of vaccination strategies in the presence and/or absence of varying degrees of pre-existing immunity.2. Determine the impact of individual and population immune profiles on the long-term effects of vaccination strategies. The long-term objective of this research program is to:3. Develop mathematical and computational methodologies for integrating disease models with biological data to enhance the understanding of complex mechanisms underlying disease emergence and spread.