Detection and characterization of critical under-immunized hotspots - Summer Undergraduate Support

关键免疫不足热点的检测和表征 麻疹等疾病疫苗接种不足的地理集群的出现已成为全国关注的问题。一些 近几个月来，尽管美国的MMR覆盖率很高（95%），但仍发生了麻疹疫情。等 疫苗接种不足的群体可以作为感染的水库，可以将疾病传播给更广泛的人群， 它们的重要性远远超出了它们的绝对数量所能显示的。这种疫苗接种不足的存在和增长 集群通常为公共卫生机构和卫生提供者网络所知，但他们通常没有足够的信息， 我们还将继续为每个此类群组中的人群提供资源，以努力提高疫苗接种率。初步结果显示， 所有疫苗接种不足的集群在引起大爆发的可能性（称为其“临界性”）方面是“相等的”， 而且一个群体中疫苗接种不足的比率并不一定与其危急程度相关。 然而，没有现有的方法来估计这种集群的潜在风险，并确定最“关键”的 一个其中一些主要原因是：（i）纯数据驱动的空间统计方法仅依赖于免疫覆盖率， 这并没有给任何迹象表明爆发的风险;和（ii）目前的因果流行病模型需要结合起来 详细的发病率数据，这是不容易获得的。 该提案汇集了系统科学方法，结合了基于代理的随机流行病模型， 机器学习、高性能计算、数据挖掘和空间统计等技术，沿着 关于免疫和发病率的公共和私人数据集，以制定一种新的方法来确定关键的集群， 通过以下任务：（i）确定免疫率明显较低或强烈抗疫苗的空间集群 （二）开发一个基于代理人的麻疹传播模型，其中包含详细的免疫接种数据， 使用新的发生率数据源进行校准;（iii）制定方法来发现和描述关键的空间集群， 考虑到不同的指标，这些指标同时捕获流行病和经济负担，并根据 （iv）使用该方法评估干预措施对关键性的影响。一个高度 一个由两所大学、一个卫生保健提供组织和一个州卫生部组成的跨学科小组将 共同开发这种方法。这些集群的特征将使公共卫生部门和 政策制定者在有针对性的监测其区域和更有效地分配资源。

Detection and characterization of critical under-immunized hotspots Emergence of undervaccinated geographical clusters for diseases like measles has become a national concern. A number of measles outbreaks have occurred in recent months, despite high MMR coverage in the United States ( 95%). Such undervaccinated clusters can act as reservoirs of infection that can transmit the disease to a wider population, magnifying their importance far beyond what their absolute numbers might indicate. The existence and growth of such undervaccinated clusters is often known to public health agencies and health provider networks, but they typically do not have enough resources to target people in each such cluster, to attempt to improve the vaccination rate. Preliminary results show that not all undervaccinated clusters are “equal” in terms of their potential for causing a big outbreak (referred to as its “criticality”), and the rate of undervaccination in a cluster does not necessarily correlate with its criticality. However, there are no existing methods to estimate the potential risk of such clusters, and to identify the most “critical” ones. Some of the key reasons are: (i) purely data-driven spatial statistics methods rely only on immunization coverage, which does not give any indication of the risk of an outbreak; and (ii) current causal epidemic models need to be combined with detailed incidence data, which has not been easily available. This proposal brings together a systems science approach, combining agent-based stochastic epidemic models, and techniques from machine learning, high performance computing, data mining, and spatial statistics, along with novel public and private datasets on immunization and incidence, to develop a novel methodology for identifying critical clusters, through the following tasks: (i) Identify spatial clusters with significantly low immunization rates, or strong anti-vaccine sentiment; (ii) Develop an agent based model for the spread of measles that incorporates detailed immunization data, and is calibrated using a novel source of incidence data; (iii) Develop methods to find and characterize critical spatial clusters, with respect to different metrics, which capture both epidemic and economic burden, and order underimmunized clusters based on their criticality; and (iv) Use the methodology to evaluate interventions in terms of their effect on criticality. A highly interdisciplinary team involving two universities, a health care delivery organization and a state department of Health, will work together to develop this methodology. Characterization of such clusters will enable public health departments and policy makers in targeted surveillance of their regions and a more efficient allocation of resources.