Optimizing Vaccination Strategies within a Population-Pathogen Landscape

建模和计算方法提供了强大的工具，在研究疾病的动态在个人和人口水平。最近的分析发展，加上信息和通信技术的进步，开辟了新的前景，并提出了新的挑战，在数学免疫学和流行病学。数学、统计和计算模型在应对这些挑战方面具有巨大潜力，可提供综合框架，使假设明确，并最佳利用有限的数据，突出决定政策需求的关键因素，提供定量预测，并评估疾病动态和控制计划中隐含的风险、收益和权衡。数学和计算模型的效用是有据可查的。一个重要的例子是，在2009年流感大流行期间实施了基于模型的疫苗接种战略和抗病毒药物使用建议。通过开发和应用这些模型，本提案旨在实现以下目标：1.在存在和/或不存在不同程度的既存免疫力的情况下，评估人口统计学对疫苗接种策略结果的影响.确定个体和群体免疫状况对疫苗接种策略长期效果的影响。本研究计划的长期目标是：3。发展数学和计算方法，将疾病模型与生物数据相结合，以加强对疾病出现和传播的复杂机制的理解。

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.