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Understanding the relationship between herd immunity and geographic scale to improve estimates of localized infectious disease outbreak risk

了解群体免疫与地理范围之间的关系，以改进对局部传染病爆发风险的估计

基本信息

批准号：
10578834
负责人：
Paul Delamater
金额：
$ 13.6万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-13 至 2025-02-28
项目状态：
未结题

项目摘要

ABSTRACT The size and frequency of outbreaks of vaccine-preventable diseases in the US are increasing. For example, although it was officially declared eliminated in 2000, there already have been as many measles cases in the US in the first five months of 2019 (940) than any full calendar year since 1994. Given trends in vaccine hesitancy, future outbreaks of measles and other vaccine-preventable diseases are all but certain to occur. Herd immunity describes the phenomenon wherein individuals without immunity from an infection are indirectly protected from that infection by immunized individuals within the population. It is an important concept for designing and monitoring vaccination campaigns and understanding infectious disease transmission dynamics. Despite its importance, a number of aspects of herd immunity remain under- or unexamined, thereby limiting its usefulness in applied epidemiological or public health settings. This K01 Award proposal focuses on herd immunity and its relationship with infectious disease outbreak risk at local geographic scales. My career goal is to become a leading scholar in the spatial epidemiology of vaccination and vaccine-preventable diseases, specializing in research that links together human behavior, policy, and disease transmission systems to understand the evolving nature of disease outbreak risk. The training activities focus on expanding my current expertise in health geography and spatial data analysis with specialized training in infectious disease epidemiology methods, agent- based modeling, and social network analysis. The proposed research program supports an interdisciplinary approach that integrates concepts and techniques from geography, epidemiology, data science and computational modeling, and public health practice to examine the complex relationships among vaccination coverage, herd immunity, geographic scale, spatial and social connectivity patterns, and disease transmission dynamics. My research aims are: 1) Evaluate approaches to define herds using network-based community detection algorithms, 2) Identify geographic scales at which the relationship between vaccination coverage and the herd immunity effect is detectable, and 3) Develop improved estimates of local disease outbreak risk by integrating potential chains of disease transmission with vaccination coverage data. My mentoring and advisory team have specialized expertise across the training and research topics, as well as experience leading interdisciplinary research teams. The outcomes of the research will be an innovative approach to define epidemiologically-relevant herds in the population, new information regarding the ability to detect the herd immunity effect across various geographic scales of analysis, and improved estimates of local infectious disease outbreak risk. The research, training, and mentoring plans proposed in this K01 award will support the development of a future R-level proposal to examine how the risk of local outbreaks of vaccine-preventable diseases evolves over space and time as changes occur in both human behaviors (e.g., vaccine refusal) and vaccine-related policy (e.g., banning exemptions from vaccination).

摘要美国疫苗可预防疾病爆发的规模和频率正在增加。比如说，虽然2000年正式宣布消灭了麻疹，但在1999年，美国在2019年的前五个月（940）比1994年以来的任何一个完整的日历年。考虑到疫苗犹豫的趋势，今后麻疹和其他疫苗可预防疾病的爆发几乎肯定会发生。群体免疫描述了一种现象，其中对感染没有免疫力的个体被间接保护免于感染。这种感染是由人群中的免疫个体引起的。这是一个重要的设计概念，监测疫苗接种运动和了解传染病传播动态。尽管重要的是，群体免疫的许多方面仍然没有得到充分或未经审查，从而限制了其效用在应用流行病学或公共卫生环境中。该K01奖提案侧重于群体免疫及其与当地地理范围内传染病爆发风险的关系。我的职业目标是成为疫苗接种和疫苗可预防疾病的空间流行病学的主要学者，专门从事将人类行为、政策和疾病传播系统联系在一起的研究，以了解疾病暴发风险的性质不断变化。培训活动的重点是扩大我目前在卫生方面的专业知识地理和空间数据分析，并接受传染病流行病学方法的专门培训，基于建模和社会网络分析。拟议的研究计划支持跨学科一种整合地理学、流行病学、数据科学和计算模型和公共卫生实践来研究疫苗接种之间的复杂关系，覆盖率、群体免疫力、地理规模、空间和社会连接模式以及疾病传播动力学本文的研究目标是：1）评价利用网络社区定义畜群的方法检测算法，2）确定疫苗接种覆盖率与群体免疫效应是可检测的，以及3）通过以下方式改进对当地疾病爆发风险的估计：将潜在的疾病传播链与疫苗接种覆盖率数据相结合。我的指导和建议团队拥有跨培训和研究主题的专业知识，以及领先的经验跨学科研究团队。研究结果将是一种创新的方法，人群中流行病学相关牛群，关于检测牛群能力的新信息不同地理分析范围内的免疫效果，以及改进对当地传染病的估计爆发风险。K01奖项中提出的研究、培训和指导计划将支持制定未来的R级建议，以研究疫苗可预防的局部疫情的风险疾病随着人类行为（例如，拒绝接种疫苗）和疫苗相关政策（例如，禁止接种疫苗）。