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: 8739003
• 负责人: KENNETH J SMITH
• 金额: $ 40.1万
• 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/8739003
• 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; Drug Formulations; Effectiveness; Egg Proteins; Elderly; Epidemic; Epidemiology; Epitopes; Equation; Equilibrium; Future; Geographic Locations; 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; Simulate; 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; public health relevance; seasonal influenza; skills; supercomputer; tool; transmission process; vaccination strategy; 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（由申请方提供）：美国流感疫苗接种政策存在问题。首先，流感疫苗的有效性低（例如，27%）在老年人中，最有可能死亡的群体。其次，最近描述了连续剂量之间的干扰。第三，疫苗配方多种多样，效价、效力、给药途径和允许使用年龄各不相同，使政策建议复杂化。第四，疫苗接种时间、年度流行和免疫持续时间之间的紧张关系是显而易见的：如果早期疫苗接种发生免疫力减弱，而晚期流行发生，保护可能会减少，而早期流行可能会在疫苗接种完成之前发生。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），基于方程的动态传输建模（EBM）和基于代理的建模（ABM）。DA为正在考虑的战略的广度提供了一个清晰的视觉框架，并且对于初始分析来说相对更快。循证医学增加了疾病传播的动力学和疫苗接种策略的间接（群体免疫）效应。ABM在超级计算机上进行，通过模拟自主的人及其在疾病传播过程中的时空人口统计和社会互动来增加更多的细节。由于ABM是计算密集型的，ABM考虑的策略将使用DA和EBM缩小。使用所有三种建模技术提供了清晰度和现实复杂性的平衡，以及在技术之间进行有效性比较的机会。目标1：确定最佳疫苗选择策略，以最大限度地减少1）美国人群和2）各种医疗实践人群中各年龄组的疾病负担和资源使用。目标二：确定每年接种疫苗的理想时机，权衡早期接种疫苗的潜在影响，免疫力减弱，流行时间，干扰和错过的疫苗接种机会。目标3：使用ABM，在美国不同地区/人群中比较灭活疫苗与潜在通用疫苗的有效性、持续时间、群体免疫力、副作用、可实现的疫苗接种率和成本的权衡，并确定有利于其采用的通用疫苗特征。该研究小组在建模方面经验丰富，拥有多种技能组合，曾共同工作，可访问流感疫苗有效性网络中的流行病学数据，并在疫苗接种问题方面拥有强大的出版记录，包括建模，成本效益分析和政策。