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: 10650190
• 负责人: Jonathan L Zelner
• 金额: $ 74.94万
• 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/10650190
• 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.

项目总结/摘要 由于流感大流行几乎没有预警，疫苗的开发和分发是在一个 比新出现的菌株传播的时间尺度更慢。同样，虽然季节性流感流行 每年都发生，它们也是众所周知的难以预测，并且需要对变化做出快速反应。 情节虽然疫苗接种，抗病毒药物和非药物干预措施（NPI）可用于 缓解这些挑战，不完善的保护和覆盖范围意味着他们的直接和间接保护， 福利取决于人口的免疫状况。因此，这一总体目标 应用程序是开发一个可持续的，可扩展的分析，预测和可视化工具管道， 将详细的临床和队列数据及时转化为关于疫苗接种、抗病毒药物使用 的NPI。我们将通过以下具体目标实现这些目标：目标1）我们将利用广泛的 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 流感预防和控制方面的问题;目标2）我们将使用 传染病传播的统计和模拟模型。具体而言，我们将目标2A）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接种的反应，以及 然后在目标2B）中，将这些信息整合到基于家庭的传播模型中，以了解其影响 这些免疫反应对流感感染易感性的影响。利用这些人的预测- 使用纵向队列数据参数化的水平模型，在目标2C）中，我们将构建合成队列 代表不同人群中免疫力的年龄特异性分布，例如密歇根州， 并利用这些数据制定针对人群的流感疫苗接种策略。在Aim 2D中，我们将 然后将这些模型的见解应用于抗病毒药物和NPI在流感中的分层应用 使用我们开发的基于网络的模拟平台来应对大流行。所有这些模型都将被设计， 与疾病预防控制中心和其他合作伙伴合作实施和分析， 建模假设和输入的准则（目标3）。这将通过自动化模型工具来增强 核查、确认和综合，以确保遵守这些标准并整合调查结果 多个建模组（目标4和5）。所有这些工具都将作为开源软件公开发布 互动工具。所有这些产品的实施都将以传达主要调查结果为目标， 以及模型输入、结构和结果的不确定性，尽可能清楚地反映在广泛的科学研究中。 和以政策为重点的利益相关者使用最先进的数据可视化工具（目标6、7和8）。成果 该项目的一个重要目标是开发一种经过验证的、系统的和协作的建模方法， 快速评估大流行性和季节性流感缓解战略。

项目成果

The Role of Staff in Transmission of SARS-CoV-2 in Long-term Care Facilities.

• DOI: 10.1097/ede.0000000000001510
• 发表时间: 2022-09-01
• 期刊: Epidemiology (Cambridge, Mass.)
• 作者: Adams C;Chamberlain A;Wang Y;Hazell M;Shah S;Holland DP;Khan F;Gandhi NR;Fridkin S;Zelner J;Lopman BA
• 通讯作者: Lopman BA

Rapid response modeling of SARS-CoV-2 transmission.

SARS-CoV-2 传播的快速响应模型。

• DOI: 10.1126/science.abp9498
• 发表时间: 2022
• 期刊: Science (New York, N.Y.)
• 作者: Zelner,Jon;Eisenberg,Marisa
• 通讯作者: Eisenberg,Marisa

There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk.

• DOI: 10.1371/journal.pcbi.1009795
• 发表时间: 2022-03
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Zelner J;Masters NB;Naraharisetti R;Mojola SA;Chowkwanyun M;Malosh R
• 通讯作者: Malosh R

Accounting for uncertainty during a pandemic.

• DOI: 10.1016/j.patter.2021.100310
• 发表时间: 2021-08-13
• 期刊: Patterns (New York, N.Y.)
• 作者: Zelner J;Riou J;Etzioni R;Gelman A
• 通讯作者: Gelman A

COVID-19 vaccination and mask wearing behaviors in the United States, August 2020 - June 2021.

• DOI: 10.1080/14760584.2022.2104251
• 发表时间: 2022-10
• 期刊: EXPERT REVIEW OF VACCINES
• 影响因子: 6.2
• 作者: Floyd, Christopher J.;Joachim, Grace E.;Boulton, Matthew L.;Zelner, Jon;Wagner, Abram L.
• 通讯作者: Wagner, Abram L.