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（由申请人提供）：美国流感疫苗接种政策存在问题。首先，流感疫苗对老年人（最有可能死亡的群体）的有效性较低（例如 27%）。其次，最近描述了连续剂量之间的干扰。第三，疫苗剂型繁多，效价、功效、给药途径和允许使用年龄各不相同，使政策建议变得复杂化。第四，疫苗接种时机、年度流行病和免疫持续时间之间的紧张关系很明显：如果疫苗接种过早，免疫力就会减弱，而疫情发生较晚，则保护作用可能会降低，而在疫苗接种完成之前，可能会出现早期流行病。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)、基于方程的动态传输建模 (EBM) 和基于代理的建模 (ABM)。 DA 为正在考虑的各种策略提供了清晰的可视化框架，并且初始分析相对较快。 EBM 增加了疾病传播的动态和疫苗接种策略的间接（群体免疫）影响。 ABM 在超级计算机上进行，通过模拟自主人员及其时空人口统计数据以及疾病在人群中传播期间的社会互动，增加了更多细节。由于 ABM 是计算密集型的，因此 ABM 考虑的策略将使用 DA 和 EBM 缩小范围。使用所有三种建模技术可以在清晰度和现实复杂性之间取得平衡，并有机会在技术之间进行有效性比较。目标 1：确定最佳疫苗选择策略，最大限度地减少 1) 美国人口和 2) 不同医疗实践人群中不同年龄组的疾病负担和资源使用。目标 2：确定年度疫苗接种的理想时机，权衡早期疫苗接种、免疫力减弱、流行时机、干扰和错过疫苗接种机会的潜在影响。目标 3：使用 ABM，比较有效性、持续时间、群体免疫、副作用、可实现的疫苗接种率以及灭活疫苗与美国不同地点/人群中潜在通用疫苗的成本，并确定有利于其采用的通用疫苗特征。该研究团队在建模方面经验丰富，拥有多种技能，能够共同合作，能够访问流感疫苗有效性网络中的流行病学数据，并且在疫苗接种问题（包括建模、成本效益分析和政策）方面拥有良好的发表记录。