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

RFA-IP-22-004，评估季节性和大流行性流感多方面控制策略的建模工具包

基本信息

批准号：
10650213
负责人：
LAUREN ANCEL MEYERS
金额：
$ 73.87万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10650213
关键词：
RFA IP 22 004 Modeling

项目摘要

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.

项目摘要我们将开发一个季节性和大流行性流感在美国传播的数据驱动模型，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，作为证据基础，流感政策制定、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输与CDC建模网络合作。该模型包括城市之间的旅行，年龄和风险组特定易感性、临床结果的可能性、干预功效和吸收率，以及当地气候和学校日历对传播率的影响。利用一系列公共卫生，流行病学，社会和经济指标，该模型可以灵活地评估成千上万的候选人干预策略，包括基于时间和地点的疫苗、抗病毒药物和社会与潜在的亚组特定优先级保持距离的措施。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，以包括通过瞬时异亚型免疫竞争的多种病毒的循环。我们将得出新的估计的持续时间和强度的异亚型免疫和设计应变特异性的策略，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抗病毒药物和联合抗病毒药物的策略。我们将适应宿主内的病毒动态新的抗病毒药物的临床数据模型，以评估不同药物方案在不同疾病中的疗效。在疾病严重程度、传染性和抗病毒药物耐药性风险方面，在目标3中，我们将建立一个基于大量数据和当地的流感传播的城市内粒度模型，与奥斯汀-朗德罗克大都会区的公共卫生和医疗保健领导者合作。我们将应用该模型来阐明流感风险和设计的社会经济和地理差异采取干预措施，缩小这些差距。在目标4中，我们将构建一个交互式可视化平台，用户可以指定流行病情景，在模拟展开时实施分层干预，并查看通过基于CDC FluView交互式门户网站的监视模块的透镜进行动态建模。我们将与CDC建模网络广泛合作，建立一个多样化的验证模型组合，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，并提供洞察社会经济差异的流感负担。而且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。