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

• 批准号: 10339412
• 负责人: Paul Delamater
• 金额: $ 13.58万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-13 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10339412
• 关键词: Advisory Committees; Affect; Algorithms; Area; Behavior; Biological Models; Calendar; California; Communicable Diseases; Communities; Complex; Computer Models; Contracts; Data; Data Analyses; Data Science; Detection; Development; Disease; Disease Outbreaks; Epidemiologic Methods; Epidemiology; Frequencies; Future; Geographic state; Geography; Goals; Health; Health Policy; Health Promotion; Herd Immunity; Human; Immunity; Immunization Programs; Immunize; Individual; Infection; Infectious Disease Epidemiology; Interdisciplinary Study; Knowledge; Link; Localized Disease; Location; Mass Vaccinations; Measles; Measles-Mumps-Rubella Vaccine; Mentored Research Scientist Development Award; Mentors; Modeling; Modernization; Monitor; Mumps; Nature; Network-based; Outcomes Research; Pathway Analysis; Pattern; Persons; Pertussis; Policies; Population; Population Surveillance; Public Health; Public Health Practice; Research; Research Training; Risk; Rubella; Social Network; Subgroup; System; Techniques; Time; Training; Training Activity; Training Programs; Vaccination; Vaccines; Work; base; burden of illness; career; design; disease transmission; dynamic system; epidemiologic data; experience; improved; innovation; interdisciplinary approach; member; programs; social; spatial epidemiology; transmission process; trend; vaccine hesitancy; vaccine refusal

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).

抽象的 美国可预防疾病的爆发的大小和频率正在增加。例如， 尽管它在2000年被正式宣布淘汰，但在 我们在2019年的前五个月（940）比1994年以来的任何完整日历年。给定疫苗犹豫的趋势， 未来的麻疹和其他可预防疾病的爆发几乎可以肯定发生。畜群免疫 描述了没有感染免疫力的个体间接保护的现象 人口中的免疫个体感染。这是设计和 监测疫苗接种运动并了解传染病的传播动态。尽管有它 重要的是，牛群免疫的许多方面仍然不足或未经检查，从而限制了其用途 在应用流行病学或公共卫生环境中。该K01奖提案的重点是群豁免权及其 与当地地理量表处的传染病爆发风险的关系。我的职业目标是成为一个 疫苗接种和疫苗预防疾病的空间流行病学领先学者，专门研究 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 疾病爆发风险不断发展的性质。培训活动的重点是扩大我目前的健康专业知识 地理和空间数据分析通过传染病流行病学方法进行专门培训，代理 基于建模和社交网络分析。拟议的研究计划支持跨学科 从地理，流行病学，数据科学和 计算建模和公共卫生实践，以检查疫苗接种之间的复杂关系 覆盖范围，牛群免疫，地理规模，空间和社会连通性模式以及疾病传播 动力学。我的研究目的是：1）评估使用基于网络的社区定义牛群的方法 检测算法，2）确定疫苗接种覆盖率之间关系的地理量表 可以检测到牛群免疫效应，3）通过通过 将疾病传播的潜在链与疫苗接种覆盖范围数据相结合。我的指导和咨询 团队在整个培训和研究主题中都有专业知识，并领导着经验 跨学科研究团队。研究结果将是定义的创新方法 在人口中，流行病学上的牛群，有关检测群体的能力的新信息 各种地理分析量表的免疫效应，并改善了局部传染病的估计值 爆发风险。该K01奖提出的研究，培训和指导计划将支持 开发未来的R级建议，以研究如何预防疫苗的局部爆发风险 随着人类行为的两种变化（例如，抗疫苗）和 与疫苗有关的政策（例如，禁止免除疫苗接种）。