Future of influenza vaccine strategies given interference and choice

描述（由申请人提供）：美国流感疫苗接种政策有问题。首先，流感疫苗在老年人中的有效性很低（例如，27%），而老年人是最容易死亡的群体。第二，最近已经描述了连续剂量之间的干扰。第三，有许多疫苗配方，其价、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明显的：如果早期接种疫苗导致免疫力下降，而后期发生流行，则保护可能会降低，而早期流行可能在疫苗接种完成之前发生。为了应对这些挑战，我们将使用互补的计算建模技术：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）。数据分析为所考虑的战略的广度提供了一个清晰的视觉框架，并且相对较快地进行初步分析。循证医学增加了疾病传播的动态和疫苗接种策略的间接（群体免疫）效应。在超级计算机上进行的ABM通过模拟自主人及其时空人口统计和疾病在人群中传播期间的社会互动，增加了进一步的细节。由于ABM是计算密集型的，ABM考虑的策略将使用数据分析和EBM来缩小范围。使用所有三种建模技术提供了现实的清晰度和复杂性的平衡，以及在技术之间执行有效性比较的机会。目标1：确定在1)美国人群和2)各种医疗实践人群的不同年龄组中最大限度地减少疾病负担和资源使用的最佳疫苗选择策略。目标2：确定年度疫苗接种的理想时机，权衡早期疫苗接种、免疫力下降以及流行时机、干扰和错过疫苗接种机会的潜在影响。目标3：使用ABM，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、副作用、可实现的疫苗接种率和成本，并确定有利于采用通用疫苗的通用疫苗特征。研究小组在建模方面经验丰富，拥有不同的技能组合，一起工作，可以访问流感疫苗有效性网络的流行病学数据，并在疫苗接种问题上有很强的出版记录，包括建模、成本效益分析和政策。

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.