Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, United States of America.
Fogarty International Center, National Institutes of Health, Bethesda, MD, United States of America.
PLoS Comput Biol. 2019 Mar 20;15(3):e1006710. doi: 10.1371/journal.pcbi.1006710. eCollection 2019 Mar.
Prophylactic vaccination is a powerful tool for reducing the burden of infectious diseases, due to a combination of direct protection of vaccinees and indirect protection of others via herd immunity. Computational models play an important role in devising strategies for vaccination by making projections of its impacts on public health. Such projections are subject to uncertainty about numerous factors, however. For example, many vaccine efficacy trials focus on measuring protection against disease rather than protection against infection, leaving the extent of breakthrough infections (i.e., disease ameliorated but infection unimpeded) among vaccinees unknown. Our goal in this study was to quantify the extent to which uncertainty about breakthrough infections results in uncertainty about vaccination impact, with a focus on vaccines for dengue. To realistically account for the many forms of heterogeneity in dengue virus (DENV) transmission, which could have implications for the dynamics of indirect protection, we used a stochastic, agent-based model for DENV transmission informed by more than a decade of empirical studies in the city of Iquitos, Peru. Following 20 years of routine vaccination of nine-year-old children at 80% coverage, projections of the proportion of disease episodes averted varied by a factor of 1.76 (95% CI: 1.54-2.06) across the range of uncertainty about breakthrough infections. This was equivalent to the range of vaccination impact projected across a range of uncertainty about vaccine efficacy of 0.268 (95% CI: 0.210-0.329). Until uncertainty about breakthrough infections can be addressed empirically, our results demonstrate the importance of accounting for it in models of vaccination impact.
预防接种是减轻传染病负担的有力工具，这是由于疫苗接种者的直接保护以及通过群体免疫对其他人的间接保护的共同作用。计算模型在制定疫苗接种策略方面发挥着重要作用，可对其对公共卫生的影响进行预测。然而，这些预测受到许多因素不确定性的影响。例如，许多疫苗效力试验侧重于测量针对疾病的保护作用，而不是针对感染的保护作用，因此疫苗接种者中的突破性感染（即疾病减轻但感染不受阻碍）的程度尚不清楚。我们在这项研究中的目标是量化对突破性感染的不确定性对疫苗接种效果的不确定性的影响程度，重点是登革热疫苗。为了真实地考虑登革热病毒（DENV）传播的许多形式的异质性，这可能对间接保护的动态产生影响，我们使用了一种基于个体的随机模型来传播 DENV，该模型是根据秘鲁伊基托斯市十多年的经验研究得出的。在常规接种 9 岁儿童疫苗（覆盖率为 80%）20 年后，针对避免疾病发作的比例的预测在对突破性感染的不确定性的范围内变化了 1.76 倍（95%CI：1.54-2.06）。这与在疫苗效力不确定性范围内预测的疫苗接种效果的变化范围（0.268（95%CI：0.210-0.329））相同。在能够通过经验解决对突破性感染的不确定性之前，我们的结果表明在疫苗接种效果模型中考虑其不确定性的重要性。
