Optimizing Vaccination Strategies within a Population-Pathogen Landscape

建模和计算方法为在个体和群体水平上研究疾病动力学提供了强大的工具。最近的分析发展，加上信息和通信技术的进步，为数学免疫学和流行病学开辟了新的前景，并提出了新的挑战。数学、统计和计算建模在应对这些挑战方面具有巨大潜力，它们为明确假设和最佳利用有限数据提供了综合框架，突出了决定政策需求的关键因素，提供了定量预测，并评估了疾病动力学和控制规划中隐含的风险、效益和权衡。数学和计算建模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基于模型的疫苗接种战略和抗病毒药物使用建议。

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.