Detection and characterization of critical under-immunized hotspots

关键免疫不足热点的检测和表征

• 批准号: 10398154
• 负责人: Achla Marathe
• 金额: $ 31.46万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398154
• 关键词: Affect; Bayesian Method; Behavioral Model; California; Characteristics; Communicable Diseases; Communities; Computer Models; Computing Methodologies; Country; Data; Data Set; Detection; Disease; Disease Clusterings; Disease Outbreaks; Disease model; Economic Burden; Economics; Epidemic; Epidemiology; Exhibits; Funding; Geography; Growth; Health; Health Personnel; Herd Immunity; High Performance Computing; Immunization; Immunize; Incidence; Individual; Infection; Intervention; Machine Learning; Measles; Measles-Mumps-Rubella Vaccine; Medical; Methodology; Methods; Minnesota; Modeling; New Jersey; New York; Oregon; Outcome; Pathway interactions; Persons; Policies; Policy Maker; Population; Population Analysis; Privatization; Public Health; Records; Reduce health disparities; Registries; Resolution; Resource Allocation; Resources; Risk; Scanning; Schools; Science; Source; System; Systems Analysis; Techniques; Time; Uncertainty; United States; Universities; Vaccinated; Vaccination; Vaccines; Washington; Work; base; data mining; demographics; diverse data; economic cost; economic outcome; health care delivery; health organization; improved; interest; novel; novel strategies; population based; provider networks; public health intervention; social; social media; spatiotemporal; statistics; tool; transmission process; vaccine hesitancy

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.

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。一个数字 尽管美国 MMR 覆盖率很高（95%），但近几个月还是爆发了麻疹疫情。这样的 疫苗接种不足的群体可能成为感染库，将疾病传播给更广泛的人群，从而放大 它们的重要性远远超出其绝对数量所表明的范围。这种未接种疫苗的人群的存在和增长 公共卫生机构和医疗服务提供者网络通常都知道集群，但他们通常没有足够的信息 针对每个此类集群中的人群提供资源，以尝试提高疫苗接种率。初步结果表明，不 所有未接种疫苗的集群在引起大爆发的可能性（称为“严重程度”）方面都是“相同的”， 集群中疫苗接种不足的比例并不一定与其严重程度相关。 然而，目前还没有方法来估计此类集群的潜在风险，并确定最“关键”的风险。 那些。一些关键原因是：(i) 纯粹数据驱动的空间统计方法仅依赖于免疫覆盖率， 没有给出任何爆发风险的迹象； (ii) 需要结合当前的因果流行病模型 详细的发病率数据并不容易获得。 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，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，以及新颖的 有关免疫和发病率的公共和私人数据集，以开发一种识别关键集群的新方法， 通过以下任务： (i) 识别免疫率极低或抗疫苗能力强的空间集群 情绪; (ii) 开发一个基于代理的麻疹传播模型，其中包含详细的免疫数据，并且 使用新的发生率数据源进行校准； (iii) 开发寻找和表征关键空间集群的方法，其中 尊重不同的指标，捕捉流行病和经济负担，并根据免疫不足的集群进行排序 论其重要性； (iv) 使用该方法来评估干预措施对关键性的影响。一个高度 涉及两所大学、一个医疗保健提供组织和一个州卫生部的跨学科团队将 共同努力开发这种方法。此类集群的特征将使公共卫生部门和 政策制定者对其地区进行有针对性的监督并更有效地分配资源。