IP20-003, Data driven transmission models to optimize influenza vaccination and pandemic mitigation strategies - COVID-19 Supplement

IP20-003，数据驱动的传播模型，以优化流感疫苗接种和大流行缓解策略 - COVID-19 补充

• 批准号: 10438198
• 负责人: Jonathan L Zelner
• 金额: $ 74.85万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 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/10438198
• 关键词: IP20; 003; Data; driven; transmission

Project Summary/Abstract Because influenza pandemics occur with little warning, vaccine development and distribution take place at a slower timescale than transmission of the emergent strain. Similarly, although seasonal influenza epidemics occur annually, they are also notoriously difficult to predict, and necessitate rapid response to changing circumstances. While vaccination, antivirals and non-pharmaceutical interventions (NPIs) are available to mitigate these challenges, imperfect protection and coverage mean that their direct and indirect protective benefits are conditional on the state of immunity in the population. Therefore, the overall objective of this application is to develop a sustainable, scalable pipeline of analytic, predictive, and visualization tools to translate detailed clinical and cohort data to into timely population-level guidance on vaccination, antiviral use, and NPIs. We will accomplish these goals through the following specific aims: Aim 1) We will use the extensive clinical and cohort data resources generated by the Michigan Influenza Center to identify and address key questions in influenza prevention and control; Aim 2) We will integrate these multiple sources of data using statistical and simulation based models of infectious disease transmission. Specifically, we will Aim 2A) use robust models of longitudinal serologic data to characterize response to natural infection and vaccination, and then in Aim 2B) integrate this information into household-based transmission models to understand the impact of these immune responses on susceptibility to influenza infection. Using the predictions of these individual- level models parameterized using longitudinal cohort data, in Aim 2C) we will construct synthetic cohorts representative of the age-specific distribution of immunity in different populations, e.g. the State of Michigan, and use these data to develop targeted population-level strategies for influenza vaccination. In Aim 2D) we will then apply the insights of these models to the layered application of antivirals and NPIs in an influenza pandemic using a network-based simulation platform we have developed. All of these models will be designed, implemented and analyzed in collaboration with CDC and other partners to ensure clearly-articulated guidelines for modeling assumptions and inputs (Aim 3). This will be augmented by tools for automated model verification, validation and synthesis which will ensure adherence to these standards and integrate the findings of multiple modeling groups (Aims 4 & 5). All of these tools will be released publicly as open-source software and interactive tools. All of these products will be implemented with the goal of communicating key findings as well as uncertainty in model inputs, structure, and outcomes as clearly as possible to a wide array of scientific and policy-focused stakeholders using state-of-the-art tools for data visualization (Aims 6,7 & 8). The outcome of this project will be the development of a validated, systematic and collaborative modeling approach tailored for rapid evaluation of both pandemic and seasonal influenza mitigation strategies.

项目摘要/摘要 由于流感大流感很少有警告，因此疫苗开发和分销发生在 时间尺度比新兴应变的传播慢。同样，尽管季节性流感流行病 每年发生，他们也很难预测它们，并需要快速响应改变 情况。疫苗接种，抗病毒药和非药物干预措施（NPI）可用于 缓解这些挑战，不完善的保护和覆盖范围意味着它们的直接和间接保护性 利益是基于人口免疫状况的条件。因此，总体目标 应用是为了开发可持续，可扩展的分析，预测和可视化工具的管道 将详细的临床和队列数据转换为及时的人口水平疫苗接种，抗病毒，使用， 和NPI。我们将通过以下特定目标实现这些目标：目标1）我们将使用广泛的 密歇根州流感中心生成的临床和队列数据资源，以识别和解决关键 预防和控制流感方面的问题；目标2）我们将使用 基于统计和模拟的传染病传播模型。具体来说，我们将目标2a）使用 纵向血清学数据的强大模型，以表征对自然感染和疫苗接种的反应，以及 然后在目标2b中）将此信息集成到家庭传输模型中以了解影响 这些免疫反应对流感感染的易感性。使用这些个人的预测 使用纵向队列数据进行参数化的级别模型，在AIM 2C中）我们将构建合成队列 代表不同人群中免疫的特定年龄分布，例如密歇根州， 并使用这些数据来开发针对性的人群疫苗接种的策略。在AIM 2D中）我们将 然后将这些模型的见解应用于抗病毒药和NPI的分层应用中 使用我们开发的基于网络的仿真平台的大流行。所有这些模型都将设计， 与CDC和其他合作伙伴合作实施和分析，以确保明确的明确 建模假设和输入的指南（AIM 3）。自动模型的工具将增加这一点 验证，验证和综合，将确保遵守这些标准并整合发现 多个建模组（目标4和5）。所有这些工具将作为开源软件公开发布 和交互式工具。所有这些产品都将实施，目的是传达关键发现 尽可能清楚地对各种科学的模型输入，结构和结果的不确定性 和以政策为中心的利益相关者使用最先进的工具进行数据可视化（目标6,7和8）。结果 该项目的开发将开发经过验证的，系统的和协作的建模方法 用于快速评估大流行和季节性流感缓解策略。