Future of influenza vaccine strategies given interference and choice

描述(申请人提供)：美国流感疫苗接种政策存在问题。首先，流感疫苗在老年人中的有效性很低(例如，27%)，老年人是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，疫苗配方繁多，其效价、效力、给药途径和允许使用年限各不相同，使政策建议复杂化。第四，接种疫苗的时间、每年的疫情和免疫持续时间之间的紧张关系是显而易见的：如果在早期接种疫苗时免疫力减弱，并发生较晚的疫情，则可能会减少保护，而在疫苗接种完成之前可能会发生早期疫情。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)。发展议程为正在审议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了初步分析。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)影响。在超级计算机上进行的ABM通过模拟自主的人及其在疾病在人群中传播期间的时空人口统计和社会互动来增加进一步的细节。由于ABM是计算密集型的，ABM考虑的策略将使用DA和EBM来缩小范围。使用所有三种建模技术提供了清晰度和现实复杂性之间的平衡，以及执行技术之间的有效性比较的机会。目的1：在1)美国人群和2)不同医疗实践人群中，确定最优的疫苗选择策略，使不同年龄段的疾病负担和资源使用最小化。目标2：确定每年接种疫苗的理想时间，权衡早期接种疫苗的潜在影响、免疫力减弱、流行时机、干扰和错过的疫苗接种机会。目的3：使用ABM，比较灭活疫苗与美国不同地区/人群中潜在通用疫苗在有效性、持续期、群体免疫力、副作用、可实现的接种率和成本方面的权衡，并确定有利于其采用的通用疫苗特性。研究团队在建模方面经验丰富，拥有不同的技能组合，曾共同工作，能够获得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良好的出版记录，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.