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).
This project focuses on herd immunity and its relationship with infectious disease outbreak risk at local geographic scales. Using measles and MMR vaccination coverage in California, I will develop and evaluate approaches to define herds within a larger population, identify the geographic scale(s) at which the effects of herd immunity are detectable, and estimate localized disease outbreak risk in the context of these findings. The outcomes of this research will support more accurate and useful estimates of local- and population-level disease outbreak risk and an improved ability to determine when, where, and why infectious disease outbreaks are likely to occur in the future.
