Modeling toolkit to evaluate multifaceted control strategies for seasonal and pandemic influenza

用于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

• 批准号: 10438127
• 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
• 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/10438127
• 关键词: Modeling; toolkit; evaluate; multifaceted; control

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.

项目总结