Dynamic data-driven decision models for infectious disease control

描述（由申请人提供）：我们的目标是通过数学建模、优化以及与公共卫生决策者的转化合作来改善传染病监测和控制。在方法上，我们将通过（i）将大规模优化、经济分析和不确定性量化整合到复杂和动态人群中疾病传播的数学模型中，以及（ii）开发面向目标的优化方法来整合不同的数据源以改进传染病监测系统，从而推进数学模型的应用，为公共卫生政策决策提供信息。我们将利用来自世界各地的流感、呼吸道合胞病毒（RSV）、百日咳、西尼罗河病毒（WNV）和登革热的数据来应用这些方法，以阐明爆发和控制的复杂驱动因素，并确定高效、经济且可行的控制政策。 我们将向公共卫生当局传播我们的模型和结果，并开发用户友好的建模工具，以促进有关有限疾病控制资源的最佳分配的准备和实时决策。因此，我们的跨学科研究将扩展传染病动态建模的方法工具包，提供更好的追踪和减轻流行病的策略，并使参与全球抗击传染病的公共卫生机构更广泛地获得科学、数据和模型。

DESCRIPTION (provided by applicant): We aim to improve infectious disease surveillance and control through mathematical modeling, optimization, and translational collaborations with public health decision makers. Methodologically, we will advance the application of mathematical modeling to inform public heath policy decisions by (i) integrating large-scale optimization, economic analyses, and uncertainty quantification into mathematical models of disease transmission in complex and dynamic populations, and by (ii) developing goal-oriented optimization methods for integrating diverse data sources to improve infectious disease surveillance systems. We will apply these approaches using data on influenza, respiratory syncytial virus (RSV), pertussis, West Nile virus (WNV), and dengue from around the world to elucidate the complex drivers of outbreaks and control and to identify highly effective, economical, and feasible control policies. We will disseminate our models and results to public health authorities and develop user-friendly modeling tools to facilitate preparedness and real-time decision- making regarding the optimal distribution of limited disease control resources. Thus, our interdisciplinary research will expand the methodological toolkit for modeling infectious disease dynamics, provide better strategies for tracking and mitigating epidemics, and make science, data, and models more broadly accessible to public health agencies engaged in the global fight against infectious diseases.