The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level—a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.

美国在 2019 年经历了历史上最高的麻疹病例数，尽管其全国麻疹疫苗接种率高于世界卫生组织建议的 95% 的接种率。自 COVID-19 大流行开始导致许多临床预防服务暂停以来，美国的儿科疫苗接种率急剧下降，大大增加了麻疹卷土重来的风险。先前的研究表明，高覆盖范围内的麻疹爆发是由未接种疫苗的空间聚类驱动的，这将局部免疫力降低到群体免疫阈值以下。然而，人们对如何最好地进行监测和有针对性的干预措施以发现和解决这些高风险领域知之甚少，并且大多数疫苗接种数据是在州一级报告的——这个决议过于粗糙，无法检测最近爆发的非疫苗接种特征的社区级聚类。在本文中，我们进行了一系列计算实验，以通过在粗略空间尺度上测量疫苗接种率来评估集群未接种疫苗对暴发潜力和预测疾病风险的偏差程度的影响。我们发现，当未接种疫苗在当地聚集时，报告州或县级的汇总数据可能会导致对爆发风险的严重低估。COVID-19 大流行揭示了美国传染病监测的弱点，以及我们对如何最好地利用详细的空间数据来中断和控制传染病传播的理解上的更大差距。