Detection and characterization of critical under-immunized hotspots

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聚集性的出现已成为一个国家关注的问题。一个数字 近几个月来，尽管美国的MMR覆盖率很高(95%)，但仍有6%的麻疹暴发发生。是这样的 接种不足的集群可能会成为感染的蓄水池，将疾病传播给更广泛的人群，放大 它们的重要性远远超出了它们的绝对数字可能表明的范围。这种疫苗接种不足的人的存在和增长 公共卫生机构和卫生服务提供者网络通常知道集群，但它们通常没有足够的 为每个这类群组的目标人群提供资源，试图提高疫苗接种率。初步结果显示， 所有接种不足的群组在其引起大暴发的可能性(称为其“危急程度”)方面是“平等的”， 而在一个集群中，疫苗接种不足的比率并不一定与其危急程度相关。 然而，目前还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”集群。 一个。其中一些关键原因是：(1)纯数据驱动的空间统计方法只依赖免疫覆盖率， 这并没有给出任何爆发风险的迹象；以及(2)需要结合当前的因果流行病模型 有详细的发病数据，这并不容易获得。 这一建议结合了系统科学的方法，结合了基于代理的随机流行病模型，以及 来自机器学习、高性能计算、数据挖掘和空间统计的技术，以及新的 关于免疫和发病率的公共和私人数据集，以开发一种新的方法来确定关键集群， 通过以下任务：(I)识别具有显著低免疫接种率或强抗疫苗的空间集群(fi 情绪；(2)开发一个以代理人为基础的麻疹传播模型，纳入详细的免疫数据，并 使用新的关联数据来源进行了校准；(3)开发了fi和表征关键空间集群的方法， 考虑到不同的指标，这些指标同时捕获了流行病和经济负担，并基于以下因素对免疫不足的集群进行排序 (Iv)使用该方法根据干预措施对关键程度的影响来评估干预措施。一位高度 由两所大学、一个医疗保健提供组织和一个州卫生部组成的跨学科团队将 共同努力发展这一方法。这种集群的特征将使公共卫生部门和 政策制定者在有针对性地监督其所在地区和更有效地分配资源方面发挥着重要作用。

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.