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

项目摘要 季节性流感估计造成4880万人患病，2270万人就医，959000人死亡， 2017- 2018年住院和79，400例死亡。流感是一种特别复杂的疾病， 流感病毒本身会随着时间的推移而变化，与麻疹或B型肝炎疫苗不同， 如果在暴发中出现的特定菌株没有被感染， 在疫苗中。此外，疫苗接种产生的免疫力随着时间的推移而下降，许多 个人，特别是老年人，可能在流感季节结束时失去保护。 iMPH：公共卫生流感建模的目标是汇集四个经验丰富的建模 包括公共卫生动态实验室（PHDL）在内的团体，该实验室作为MIDAS拥有丰富的经验 匹兹堡疫苗研究组（Pittsburgh Vaccine Research Group）基于代理的流感建模卓越中心 （PittVax），疫苗政策专家，他们是住院和门诊CDC流感疫苗的研究中心负责人 流行病学预测的理论与实践（德尔菲） CMU的小组，美国最准确的流感预测小组，以及目前的MIDAS网络协调小组 中心（MCC），带来了主要的数据和模型协调专业知识。 我们的具体目标是建立现实的，基于生物学的流感模型，包括发展和 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， 可以测试不同预防策略在季节性或大流行性流感中的益处。具体来说，我们将 研究策略，如：增强疫苗，创造高初始抗体水平，增加一个 第二，季节中期疫苗，或可能延迟某些个体的疫苗接种，直到季节后期。 我们还将研究多年来疫苗接种政策的有效性，例如不同的 为以前患过流感的人接种疫苗，并考虑每隔一年接种一次疫苗， 最后，我们将研究社区一级的干预措施，如关闭学校和在家工作， 影响流感在一个季节的传播。 作为CDC流感建模中心，我们还将与其他中心合作， 当前的流感威胁，分享数据，模型和结果，为CDC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.