National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
PLoS One. 2010 Sep 17;5(9):e12777. doi: 10.1371/journal.pone.0012777.
Because they can generate comparable predictions, mathematical models are ideal tools for evaluating alternative drug or vaccine allocation strategies. To remain credible, however, results must be consistent. Authors of a recent assessment of possible influenza vaccination strategies conclude that older children, adolescents, and young adults are the optimal targets, no matter the objective, and argue for vaccinating them. Authors of two earlier studies concluded, respectively, that optimal targets depend on objectives and cautioned against changing policy. Which should we believe?
In matrices whose elements are contacts between persons by age, the main diagonal always predominates, reflecting contacts between contemporaries. Indirect effects (e.g., impacts of vaccinating one group on morbidity or mortality in others) result from off-diagonal elements. Mixing matrices based on periods in proximity with others have greater sub- and super-diagonals, reflecting contacts between parents and children, and other off-diagonal elements (reflecting, e.g., age-independent contacts among co-workers), than those based on face-to-face conversations. To assess the impact of targeted vaccination, we used a time-usage study's mixing matrix and allowed vaccine efficacy to vary with age. And we derived mortality rates either by dividing observed deaths attributed to pneumonia and influenza by average annual cases from a demographically-realistic SEIRS model or by multiplying those rates by ratios of (versus adding to them differences between) pandemic and pre-pandemic mortalities.
In our simulations, vaccinating older children, adolescents, and young adults averts the most cases, but vaccinating either younger children and older adults or young adults averts the most deaths, depending on the age distribution of mortality. These results are consistent with those of the earlier studies.
由于数学模型能够做出类似的预测，因此它们是评估药物或疫苗分配策略的理想工具。然而，为了保持可信度，结果必须一致。最近对流感疫苗接种策略进行评估的作者得出结论，无论目标是什么，年龄较大的儿童、青少年和年轻成年人都是最佳目标，并主张对他们进行接种。两项早期研究的作者分别得出结论，最佳目标取决于目标，并警告不要改变政策。我们应该相信哪一个？
在以年龄划分的人与人之间的接触矩阵中，主对角线总是占主导地位，反映了同龄人之间的接触。间接效应（例如，接种一个群体对其他人的发病率或死亡率的影响）是由非对角线元素产生的。基于与其他人接近的时期混合矩阵具有更大的次对角线和超对角线，反映了父母与子女之间的接触以及其他非对角线元素（例如，反映了同事之间年龄独立的接触），而不是基于面对面交流的矩阵。为了评估靶向疫苗接种的影响，我们使用了时间使用研究的混合矩阵，并允许疫苗效力随年龄变化。我们通过将归因于肺炎和流感的观察到的死亡人数除以来自人口统计学上逼真的 SEIRS 模型的平均年度病例数，或者通过将这些比率乘以（而不是将其添加到）大流行和大流行前死亡率之间的差异，来得出死亡率。
在我们的模拟中，接种年龄较大的儿童、青少年和年轻成年人可以避免最多的病例，但根据死亡率的年龄分布，接种年龄较小的儿童和年龄较大的成年人或年轻成年人可以避免最多的死亡。这些结果与早期研究的结果一致。
