Future of influenza vaccine strategies given interference and choice

考虑到干扰和选择，流感疫苗策略的未来

• 批准号: 9065582
• 负责人: KENNETH J SMITH
• 金额: $ 37.69万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9065582
• 关键词: Accounting; Address; Adoption; Adverse effects; Age; Attenuated; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Child; Complement; Computer Simulation; Cost Effectiveness Analysis; Coupled; Data; Decision Analysis; Decision Making; Development; Disease; Dose; Effectiveness; Egg Proteins; Elderly; Epidemic; Epidemiology; Epitopes; Equation; Equilibrium; Future; Geographic Locations; Health; Herd Immunity; Hypersensitivity; Immunity; Immunologics; Inactivated Vaccines; Influenza; Influenza vaccination; Intervention; Intramuscular; Licensing; Life; Location; Medical; Methods; Modeling; Morbidity - disease rate; Nature; Outcome; Pattern; Persons; Policies; Policy Maker; Population; Publications; Recommendation; Research; Resources; Route; Seasons; Social Interaction; Structure; Techniques; Time; Vaccinated; Vaccination; Vaccines; Visual; Weather; Work; age group; base; burden of illness; cohort; cost; cost effective; cost effectiveness; demographics; disease transmission; epidemiologic data; experience; expiration; flu; high risk; immunogenic; improved; influenza epidemic; influenza virus vaccine; mathematical model; mortality; programs; seasonal influenza; skills; supercomputer; tool; transmission process; vaccination strategy; vaccine development; vaccine effectiveness

DESCRIPTION (provided by applicant): US influenza vaccination policy is problematic. First, influenza vaccine effectiveness is low (e.g., 27%) in the elderly, the group most likely to die. Second, interference between successive doses has recently been described. Third, there are many vaccine formulations, with differing valences, efficacies, administration routes, and allowable ages of use, complicating policy recommendations. Fourth, the tension between the timing of vaccination, annual epidemics, and duration of immunity is clear: if waning immunity occurs with early vaccination and a late epidemic occurs, protection may be reduced whereas an early epidemic may occur before vaccination is completed. To address these challenges, we will use complimentary computational modeling techniques: Markov cohort decision analysis (DA), equation-based dynamic transmission modeling (EBM), and agent-based modeling (ABM). DA provides a clear visual framework for the breadth of strategies under consideration and is relatively quicker for initial analyses. EBM adds to this the dynamics of disease transmission and indirect (herd immunity) effects of vaccination strategies. ABM, conducted on supercomputers, adds further detail through simulating autonomous persons and their spatial and temporal demographics and social interactions during disease spread through a population. Because ABM is computationally intensive, strategies considered by ABM will be narrowed using DA and EBM. Using all three modeling techniques offers a balance of clarity and the complexity of reality, as well as the opportunity to perform validity comparisons between techniques. Aim 1: Determine the optimal vaccine selection strategy that minimizes disease burden and resource use in various age groups in 1) the US population and 2) various medical practice populations. Aim 2: Determine the ideal timing of annual vaccination, weighing the potential impact of early vaccination, waning immunity, and epidemic timing, interference, and missed vaccination opportunities.. Aim 3: Using ABM, compare the trade-offs of effectiveness, duration, herd immunity, side effects, achievable vaccination rates, and cost of inactivated vaccines to those of potential universal vaccines in different US locations/populations and determine universal vaccine characteristics that favor its adoption. The research team is experienced in modeling, possesses diverse skill sets, has worked together, has access to epidemiologic data in the Influenza Vaccine Effectiveness Network, and has a strong publication record in vaccination issues, encompassing modeling, cost- effectiveness analysis, and policy.

描述（由申请人提供）：美国流感疫苗接种政策是有问题的。首先，在老年人中，流感疫苗的有效性很低（例如27％），最有可能死亡。其次，最近描述了连续剂量之间的干扰。第三，有许多疫苗配方，具有不同的价，效力，管理路线和允许使用年龄，使政策建议复杂化。第四，疫苗接种时间，年流行病和免疫持续时间之间的张力很明显：如果随着早期疫苗接种而发生免疫力和流行晚期，则可能会降低保护，而在疫苗接种完成之前可能会发生早期流行病。为了应对这些挑战，我们将使用免费的计算建模技术：马尔可夫队列决策分析（DA），基于方程式的动态传输建模（EBM）和基于代理的建模（ABM）。 DA为正在考虑的策略提供了一个清晰的视觉框架，并且对于初始分析而言相对较快。 EBM为疫苗接种策略的疾病传播和间接免疫效应的动态增加了。在超级计算机上进行的ABM通过模拟自治人员及其空间和时间的人口统计学以及通过人群传播期间的空间和时间人口统计以及社交互动来增加细节。由于ABM是计算密集型的，因此ABM考虑的策略将使用DA和EBM缩小。使用所有三种建模技术，都可以平衡清晰度和现实的复杂性，以及在技术之间进行有效性比较的机会。目标1：确定最佳的疫苗选择策略，以最大程度地减少疾病负担和资源的使用。 Aim 2: Determine the ideal timing of annual vaccination, weighing the potential impact of early vaccination, waning immunity, and epidemic timing, interference, and missed vaccination opportunities.. Aim 3: Using ABM, compare the trade-offs of effectiveness, duration, herd immunity, side effects, achievable vaccination rates, and cost of inactivated vaccines to those of potential universal vaccines in different US locations/populations and determine universal vaccine有利于采用的特征。研究团队在建模，拥有多样化的技能，共同努力，可以在流感疫苗有效性网络中获得流行病学数据，并且在疫苗接种问题中具有强大的发布记录，包括建模，成本效益分析和政策。