Addressing Gaps in Data and Methods in Measles Burden Estimation

Vaccination against measles has been available for decades, although still a substantial number of global cases and deaths persist. Strategies to reach measles elimination goals require a more comprehensive understanding of the patterns of immunity and burden across locations, time, and age in local contexts throughout the world, particularly in low- and middle-income countries (LMICs). These challenges, in part, can be addressed via a thorough examination of all available data on measles immunity, cases, and deaths and synthesizing these data streams through the development of novel mathematical and statistical models. The overall aims of this thesis are to (A) improve upon and better understand the data available for modellers interested in estimating measles susceptibility, incidence, or mortality in LMICs, and (B) develop improved methodology for generating more robust estimates using these data, including by dimensions of age, space, and time. To accomplish these aims, this thesis first identified all data on measles seroprevalence and characterized bias within each primary study. Next, this thesis explored subnational measles case notifications in Ethiopia and tested multiple methodologic strategies for fitting dynamic transmission models with these data while accounting for various case ascertainment rates. Then, to aid in developing more robust models of measles mortality, this thesis outlined activities following an expert consultation to establish a conceptual framework of population-level factors related to measles case fatality and a literature review of evidence of an 4 association between related indicators and case fatality. Finally, to quantify the heterogeneity in measles case fatality temporally, in different locations and across the lifespan, this thesis estimated country-, year-, and age-specific case-fatality via a meta-regression model using all available literature and identified indicators as covariates. Altogether, this thesis addressed gaps across challenges related to measles burden estimation.