RFA-IP-22-004, Modeling toolkit to evaluate multifaceted control strategies for seasonal and pandemic influenza

项目总结 我们将开发一个数据驱动的美国季节性和大流行性流感传播模型，以 加快对多方面流感干预战略的有力评估。我们将与 CDC建模网络，以提高模型的保真度、透明度和翻译性，作为证据基础 流感政策的制定、预防和控制。该项目扩展了流感的集合种群模型。 我们正在开发的美国217个主要大都市地区内部和之间的传播 与疾病预防控制中心建模网络的协作。该模式包括城市之间、年龄和风险群体之间的旅行 特异性敏感性、临床结果的概率、干预效果和摄取率以及 当地气候和校历对传播率的影响。利用一系列的公共卫生， 流行病学、社会和经济指标，该模型可以灵活地评估数千名候选人 干预策略，包括基于时间和地点的疫苗、抗病毒药物和社会服务的组合 通过潜在的特定于小组的优先顺序来疏远措施。 我们的建议包括四个主要目标。在目标1中，我们将扩展我们的美国流感模型，将联合- 通过瞬时异亚型免疫竞争的多种病毒的循环。我们将得出新的估算值 对于异亚型免疫的持续时间和幅度，并设计有效的毒株特异性策略 控制共同传播的季节性和大流行性流感病毒。在目标2中，我们将评估干预措施 利用新批准的和联合使用的抗病毒药物的战略。我们将适应宿主内的病毒动态 新型抗病毒药物临床数据模型评价不同药物方案在不同疾病中的疗效 关于疾病严重性、传染性和抗病毒耐药性风险的亚群。在《目标3》中， 我们将在丰富的数据和本地的基础上，建立流感在城市内传播的颗粒状模型 与奥斯汀-圆石城地区的公共卫生和医疗保健领导者合作。我们会 应用该模型来阐明流感风险和设计中的社会经济和地理差异 改善这种差距的干预措施。在AIM 4中，我们将构建一个交互式可视化平台，允许 用户可以指定流行情景，在模拟展开时实施分层干预，并查看 通过基于疾病预防控制中心的FluView互动门户的监视模块的镜头进行动态建模。 我们将与CDC建模网络广泛合作，构建各种经过验证的模型和产品组合 协作决策支持的最佳实践。我们的项目将为评估提供灵活的模型 多方面的流感干预措施，阐明流感病毒之间的竞争和 新的抗病毒药物，并提供了对流感负担的社会经济差异的见解。此外，我们的 创新的可视化工具将广泛支持将科学转化为公共政策。

PROJECT SUMMARY We will develop a data-driven model of seasonal and pandemic influenza transmission throughout the US to accelerate robust assessments of multifaceted influenza intervention strategies. We will work closely with the CDC Modeling Network to advance the fidelity, transparency and translation of models as an evidence base for influenza policy making, prevention and control. This project extends a metapopulation model of influenza transmission within and between 217 major metropolitan areas in the US that we are developing in collaboration with the CDC Modeling Network. The model includes travel between cities, age- and risk-group specific susceptibility, probability of clinical outcomes, intervention efficacies and uptake rates, as well as the impacts of local climate and school calendars on transmission rates. Using a range of public health, epidemiological, societal and economic metrics, the model can flexibly evaluate thousands of candidate intervention strategies, including time- and location-based combinations of vaccines, antivirals, and social distancing measures with potential subgroup-specific prioritization. Our proposal includes four major aims. In Aim 1, we will extend our US Influenza Model to include the co- circulation of multiple viruses competing via transient heterosubtypic immunity. We will derive new estimates for the duration and magnitude of heterosubtypic immunity and design strain-specific strategies for effectively controlling co-circulating seasonal and pandemic influenza viruses. In Aim 2, we will evaluate intervention strategies that leverage newly approved and combined antiviral drugs. We will fit within-host viral dynamic models to clinical data on new antivirals to estimate the efficacy of various drug regimens in different subpopulations with respect to disease severity, infectiousness, and the risk of antiviral resistance. In Aim 3, we will build a granular within-city model of influenza transmission based on abundant data and local collaborations with public health and healthcare leaders in the Austin-Round Rock Metropolitan Area. We will apply the model to elucidate socioeconomic and geographic disparities in influenza risk and design interventions that ameliorate such gaps. In Aim 4, we will build an interactive visualization platform that allows users to specify epidemic scenarios, implement layered interventions as simulations unfold, and view the model dynamics through the lens of a surveillance module based on the CDC’s FluView Interactive portal. We will work extensively with the CDC Modeling Network to build a diverse portfolio of validated models and best practices for collaborative decision support. Our projects will contribute flexible models for the evaluation of multifaceted influenza interventions, elucidate competition among influenza viruses and the efficacies of novel antivirals, and provide insights into socioeconomic disparities in influenza burden. Furthermore, our innovative visualization tool will broadly support the translation of science to public policy.