Individual-based Simulation of Seasonal and Pandemic Influenza Epidemics - supplement Emergency Response to the COVID-19 Pandemic

项目摘要 据估计，季节性流感导致4880万人患病，2270万人次就诊，95.9万人次 2017-18年度住院人数和79,400人死亡。流感是一种特别复杂的疾病，要预防为 流感病毒本身会随着时间的推移而变化，与麻疹或乙肝疫苗不同， 如果暴发中出现的特定毒株没有出现，则年度疫苗接种的有效性可能会受到限制 在疫苗中有代表性。此外，接种疫苗产生的免疫力会随着时间的推移而下降，许多 个人，特别是老年人，可能会在流感季节接近尾声时失去保护。 IMPH：公共卫生流感模型的目标是将四个有经验的模型结合在一起 包括公共卫生动态实验室(PHDL)在内的组织拥有MIDAS的重要经验 匹兹堡疫苗研究小组，基于代理人的流感建模的卓越中心 (PittVax)，在住院和门诊疾控中心流感疫苗方面处于现场领先地位的疫苗政策专家 有效性(VE)网络，Delphi(发展流行病预测的理论和实践) CMU的小组，美国最准确的流感预测小组，以及目前的MIDAS网络协调 该中心(MCC)提供了重要的数据和模型协调专业知识。 我们的具体目标是创建现实的、基于生物学的流感模型，包括开发和 随着时间的推移，通过疾病的发展或接种疫苗来维持免疫力，以便我们 可以测试季节性或大流行性流感不同预防策略的益处。具体来说，我们将 检查战略，如：产生高初始抗体水平的增强型疫苗，增加 第二，季节中期疫苗，或可能将一些人的疫苗接种推迟到季节晚些时候。 我们还将检查疫苗接种政策在多年时间内的有效性，例如不同的 为以前患过流感的个人接种疫苗，并考虑每隔一年接种一次疫苗， 最后，我们将研究社区层面的干预措施，例如关闭学校和在家工作 影响流感在一个季节的传播。 作为疾控中心的流感建模中心，我们还将与其他中心合作，开发快速响应 当前的流感威胁，分享数据、模型和结果，为疾控中心提供更高的信心 基于模型的建议可用于制定地方、州和国家的流感政策。

Project Abstract Seasonal influenza caused an estimated 48.8 million illnesses, 22.7 million health care visits, 959,000 hospitalizations, and 79,400 deaths in 2017-18. Influenza is a particularly complicated disease to prevent as the influenza virus itself changes over time, and unlike vaccinations for measles or hepatitis B, the effectiveness of annual vaccination may be limited if the particular strain that appears in an outbreak is not represented in the vaccine. In addition, the immunity produced by vaccination declines over time, and many individuals, especially the elderly, may lose protection toward the end of an influenza season. The goals of iMPH: Influenza Modeling for Public Health are to bring together four experienced modeling groups including the Public Health Dynamics Laboratory (PHDL) that has significant experience as a MIDAS Center of Excellence in the agent-based modeling of influenza, the Pittsburgh Vaccine Research Group (PittVax), vaccine policy experts who are site leads in both the inpatient and outpatient CDC influenza vaccine effectiveness (VE) networks, the DELPHI (Developing the Theory and Practice of Epidemiological Forecasting) group at CMU, the most accurate US influenza prediction group, and the current MIDAS Network Coordination Center (MCC) that brings major data and model coordination expertise. Our specific Aims are to create realistic, biologically based models of influenza, including the development and maintenance of immunity over time through either the development of the disease or vaccination so that we can test the benefit of different prevention strategies in seasonal or pandemic influenza. Specifically, we will examine strategies such as: enhanced vaccines that create high initial antibody levels, the addition of a second, mid-season vaccine, or potentially delaying vaccination in some individuals until later in the season. We will also examine the effectiveness of vaccination policies over a multi-year time, such as different vaccinations for individuals who have had previous influenza, and consideration of every-other year vaccines, Finally we will examine community-level interventions, such as school closures and working from home to impact the spread of influenza over a season. As a CDC Influenza Modeling Center, we will also collaborate with other centers to develop rapid responses to current influenza threats, to share data, models and results to provide higher confidence to the CDC that model-based recommendations can be used to formulate local, state and national influenza policy.