Understanding the Resurgence of an Eliminated Disease: Spatial, Attitudinal, and Regulatory Factors Underlying Measles Outbreaks in the Post-Elimination Era by Nina Brooks Masters A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Epidemiological Science) in the University of Michigan 2021 Doctoral Committee: Professor Matthew L. Boulton, Chair Assistant Professor Paul L. Delamater, University of North Carolina Associate Professor Marisa C. Eisenberg Associate Professor David Hutton Assistant Professor Jon Zelner Nina B. Masters [email protected] ORCID iD: 0000-0002-3155-6058 © Nina B. Masters, 2021 Dedication To my parents: thank you for teaching me to pursue my dreams, humoring my childhood love of infectious diseases, and, most importantly, vaccinating me. ii Acknowledgements There are many individuals without whom I could not have completed this dissertation and to whom I am so very grateful. First, I would like to thank my committee chair, Dr. Matthew Boulton. You helped me become a better scientific writer and communicator during the MPH and PhD, told me to pursue the research questions that interested me most, and gave me the academic and intellectual freedom to do so. I also want to thank Dr. Jon Zelner, who nurtured and encouraged my passion for infectious disease modeling, pushed me to become fluent in R and Github, and believed in my coding skills long before I did. Without your mentorship, guidance, and hours of reading code, Jon, this dissertation wouldn’t have been possible. I am also grateful to my other committee members, Dr. Marisa Eisenberg, Dr. Paul Delamater, and Dr. David Hutton. Marisa, without your courses on infectious disease modeling, I never would have ended up as a modeler, and I so appreciate your mentorship, humor, and technical expertise. Paul, I am so glad I cold-emailed you after reading some of your papers on vaccine exemptions, and grateful to now know all about the field of medical geography! David, I deeply appreciate you helping me understand the policy implications of my research. I also want to thank Dr. Matthew Kay for your mentorship in the world of data visualization and information storytelling that has really helped me to communicate my research effectively. iii Thank you also to the members of the EpiBayes Research Group, who made me feel like I was part of a team, and who gave me a research community, especially during the strange, lonely months of the COVID-19 pandemic. A special shout out to Ryan Malosh for your immeasurable advice and friendship, and to Rob Trangucci, for hours of zoom calls explaining advanced Bayesian statistical concepts. And to the rest of my SPH colleagues, past and present, you helped me get through this process in one piece! I am so grateful to my friends at Michigan who have supported me throughout this journey. To my GEO co-stewards in the School of Public Health, especially Ben, Zak, and Kat, thank you for being such a powerful support system throughout my time at Michigan. To the student organizers who co-led our efforts to get asynchronous classes on Election Day – thank you for helping to do our part to turn Michigan Blue! Megan – thank you for countless dinners, discussions, and adventures – you made our weird little corner of Ann Arbor feel like home. Finally, thank you to my family. Thank you for championing my successes, for celebrating (and reading!) my publications, and for helping me through the rejections and setbacks along the way. I would not have achieved my PhD without your steadfast support. To my sister Julia, I am especially grateful for the year and a half we shared in Ann Arbor during your MSW, and your support during the dark days of Comps preparation. Matt, thank you for standing by my side these three years – despite the distance (and the pandemic). I don’t know where I’d be without your commitment to cheering me on, making me smile, and always supporting me when times are tough. iv Table of Contents Dedication ....................................................................................................................................... ii Acknowledgements ........................................................................................................................ iii List of Tables ................................................................................................................................. ix List of Figures ............................................................................................................................... xii List of Equations ......................................................................................................................... xvii List of Appendices ..................................................................................................................... xviii List of Abbreviations ................................................................................................................... xix Abstract ......................................................................................................................................... xx Introduction .................................................................................................................... 1 1.1 Specific aims and hypotheses ................................................................................................ 2 1.2 Background and significance ................................................................................................ 4 1.2.1 Global measles resurgence ............................................................................................. 4 1.2.2 Spatial factors: clustering of non-vaccinators ................................................................ 9 1.2.3 Attitudinal factors: vaccine hesitancy ........................................................................... 15 1.2.4 Regulatory factors: vaccine mandates and exemptions ................................................ 23 1.2.5 Goals of this dissertation .............................................................................................. 31 v Does Fine-Scale Spatial Clustering of Measles Non-Vaccination Increase Outbreak Potential? A Simulation-Based Study of the Impacts of Heterogeneous Non-Vaccination and Aggregated Reporting Data .......................................................................................................... 34 2.1 Significance statement ......................................................................................................... 34 2.2 Abstract ............................................................................................................................... 35 2.3 Introduction ......................................................................................................................... 36 2.3.1 Redefining vaccination coverage targets ...................................................................... 37 2.3.2 What is the right scale of surveillance? ........................................................................ 38 2.4 Methods ............................................................................................................................... 39 2.4.1 Simulated environment ................................................................................................. 39 2.4.2 Clustering motifs of non-vaccination ........................................................................... 40 2.4.3 Model structure ............................................................................................................. 40 2.4.4 Measuring clustering .................................................................................................... 41 2.4.5 Measuring aggregation effects ...................................................................................... 41 2.4.6 Statistical analysis and simulation protocol .................................................................. 42 2.4.7 Sensitivity analysis ....................................................................................................... 43 2.5 Results ................................................................................................................................. 43 2.5.1 Impact of clustering on outbreak probability and size ................................................. 43 2.5.2 Impact of clustering on outbreak risk and magnitude .................................................. 44 2.5.3 Impact of measurement scale on outbreak size prediction errors ................................. 45 2.6 Discussion ........................................................................................................................... 46 2.6.1 Strengths and limitations .............................................................................................. 49 2.7 Conclusions ......................................................................................................................... 50 Measuring Multiple Dimensions of Non-Vaccination Clustering in Michigan from 2008-2018 ..................................................................................................................................... 64 3.1 Significance statement ......................................................................................................... 64 vi 3.2 Abstract ..............................................................................................................................