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

项目摘要/摘要 由于流感大流行在没有预警的情况下发生，疫苗的开发和分发是在一个 比紧急应变传播更慢的时间尺度。同样，尽管季节性流感疫情 虽然每年都会发生，但众所周知，它们也很难预测，需要对变化做出快速反应。 情况。虽然疫苗接种、抗病毒药物和非药物干预(NPI)可用于 缓解这些挑战，不完善的保护和覆盖意味着它们的直接和间接保护 福利取决于人群中的免疫力状态。因此，这一总体目标是 应用程序是开发可持续、可扩展的分析、预测和可视化工具管道，以 将详细的临床和队列数据转化为及时的人群层面的疫苗接种、抗病毒使用、 和NPI。我们将通过以下具体目标实现这些目标：目标1)我们将广泛使用 由密歇根流感中心生成的临床和队列数据资源，用于识别和解决关键 流感预防和控制中的问题；目标2)我们将使用以下工具整合这些多个数据来源 基于统计和模拟的传染病传播模型。具体地说，我们将瞄准使用 纵向血清学数据的稳健模型，以表征对自然感染和疫苗的反应，以及 然后在目标2B)将这些信息整合到基于家庭的传播模型中，以了解其影响 这些免疫反应与流感感染的易感性有关。利用这些个人的预测- 使用纵向队列数据进行参数化的水平模型，目标2C)我们将构建合成队列 代表免疫在不同人群中的特定年龄分布，例如密歇根州， 并利用这些数据为流感疫苗接种制定有针对性的人群水平战略。在Aim 2D中)我们将 然后将这些模型的洞察力应用于流感中抗病毒药物和NPI的分层应用 使用我们开发的基于网络的仿真平台。所有这些模型都将被设计， 与疾控中心和其他合作伙伴协作实施和分析，以确保清晰明了 假设和投入建模准则(目标3)。这一点将通过用于自动化模型的工具来增强 核查、确认和综合，以确保遵守这些标准，并整合调查结果 多个模型组(目标4和5)。所有这些工具都将以开源软件的形式公开发布 和交互工具。所有这些产品都将实施，目的是将关键发现传达为 以及模型输入、结构和结果中的不确定性尽可能清楚地提供给广泛的科学研究人员 和注重政策的利益攸关方使用最先进的数据可视化工具(目标6、7和8)。结果是 该项目将开发一种经过验证的、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战略。

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.