Walters Magdalene, Perkins T Alex
Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556, USA.
Infect Dis Model. 2020 Oct 1;5:783-797. doi: 10.1016/j.idm.2020.09.008. eCollection 2020.
The CYD-TDV vaccine was recently developed to combat dengue, a mosquito-borne viral disease that afflicts millions of people each year throughout the tropical and subtropical world. Its rollout has been complicated by recent findings that vaccinees with no prior exposure to dengue virus (DENV) experience an elevated risk of severe disease in response to their first DENV infection subsequent to vaccination. As a result of these findings, guidelines for use of CYD-TDV now require serological screening prior to vaccination to establish that an individual does not fall into this high-risk category. These complications mean that the public health impact of CYD-TDV vaccination is expected to be higher in areas with higher transmission. One important practical difficulty with tailoring vaccination policy to local transmission contexts is that DENV transmission is spatially heterogeneous, even at the scale of neighborhoods or blocks within a city. This raises the question of whether models based on data that average over spatial heterogeneity in transmission could fail to capture important aspects of CYD-TDV impact in spatially heterogeneous populations. We explored this question with a deterministic model of DENV transmission and CYD-TDV vaccination in a population comprised of two communities with differing transmission intensities. Compared to the full model, a version of the model based on the average of the two communities failed to capture benefits of targeting the intervention to the high-transmission community, which resulted in greater impact in both communities than we observed under even coverage. In addition, the model based on the average of the two communities substantially overestimated impact among vaccinated individuals in the low-transmission community. In the event that the specificity of serological screening is not high, this result suggests that models that ignore spatial heterogeneity could overlook the potential for harm to this segment of the population.
CYD-TDV疫苗是最近研发出来用于对抗登革热的，登革热是一种由蚊子传播的病毒性疾病，每年在热带和亚热带地区折磨着数百万人。其推广因最近的研究结果而变得复杂，即以前没有接触过登革热病毒（DENV）的疫苗接种者在接种疫苗后首次感染DENV时，患严重疾病的风险会升高。由于这些研究结果，CYD-TDV的使用指南现在要求在接种疫苗前进行血清学筛查，以确定个体不属于这一高风险类别。这些复杂情况意味着CYD-TDV疫苗接种对公共卫生的影响在传播率较高的地区预计会更大。根据当地传播情况调整疫苗接种政策的一个重要实际困难是，DENV传播在空间上是异质的，即使在城市内的社区或街区层面也是如此。这就提出了一个问题，即基于传播空间异质性平均数据的模型是否可能无法捕捉CYD-TDV在空间异质人群中的重要影响方面。我们用一个确定性模型探讨了这个问题，该模型模拟了由两个传播强度不同的社区组成的人群中的DENV传播和CYD-TDV疫苗接种情况。与完整模型相比，基于两个社区平均值的模型版本未能捕捉到将干预措施针对高传播社区的益处，这导致两个社区的影响都比我们在均匀覆盖下观察到的更大。此外，基于两个社区平均值的模型大幅高估了低传播社区中接种疫苗个体的影响。如果血清学筛查的特异性不高，这一结果表明，忽略空间异质性的模型可能会忽视对这部分人群造成伤害的可能性。
