Integrative Modeling of study design and transmission dynamics to infer epidemic drivers and inform decision-making: Applications to HIV and other emerging pathogens

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PROJECT SUMMARY/ABSTRACT CANDIDATE. This proposed 5-year NIAID K01 grant will support the research and career development of Dr. Steven Bellan, a Postdoctoral Fellow in the Center for Computational Biology and Bioinformatics (CCBB) at The University of Texas at Austin (UT). Dr. Bellan's long-term career goal is to become a leader in the use of high performance computing to identify and solve infectious disease problems. Dr. Bellan's research helps plan and interpret epidemiological studies by accounting for study design-driven biases with innovative simulation methods that explicitly model empirical studies as observation processes superimposed over transmission processes. His background in epidemiology, statistics, mathematics, and disease ecology make Dr. Bellan uniquely qualified to contribute significantly to infectious disease epidemiology at the nexus between transmission modeling and epidemiological study design. His research has already led to key insights into HIV epidemiology and helped the CDC plan their recent Ebola vaccine trial. Dr. Bellan's short-term goals during the award are to build relationships with new mentors and collaborators, to publish scientific manuscripts to boost his already strong publication record, and to develop training in four new areas: (1) cutting-edge methods in computational statistics; (2) clinical trial ethics; (3) decision-support tool development; and (4) HIV policy. He will gain this training through guided self-study, courses at UT, online courses from Harvard and Georgetown, and a summer workshop at the University of Washington. Dr. Bellan's development into a successful independent investigator will be guided by a diverse mentorship committee with expertise in transmission modeling, study design, computational statistics, HIV epidemiology and policy, decision-support tool development, and bioethics: Drs. Lauren Meyers (UT), Mike Daniels (UT), Brian Williams (Stellenbosch), and Rieke van der Graaf (Utrecht Medical Center). With this training, Dr. Bellan will also advance his ability to train others, in particular, through his role teaching workshops on quantitative methods in infectious disease epidemiology to students, researchers, and public health professionals in Africa and the US since 2009. ENVIRONMENT. UT is an excellent setting for a mentored career award to Dr. Bellan because of its emphasis on integrating biological, epidemiological, and statistical research to understand infectious diseases, as evidenced by its CCBB and Center for Infectious Diseases, which foster collaboration between researchers from diverse departments. The Dept. of Population Health at UT's incipient Dell Medical School provides a unique opportunity for Dr. Bellan to forge collaborations with clinical researchers during the nascent stage of a biomedical research hub. Finally, UT's computational resources are world class; Dr. Bellan will leverage UT's Texas Advanced Computing Center, one of the most powerful computing resources in the world, to bring the computational advances of the last decade to key questions in infectious disease epidemiology. This unique environment will help ensure that Dr. Bellan develops into a successful independent investigator. RESEARCH. The goals of the proposed research are to illuminate the drivers of HIV epidemic variation across sub-Saharan Africa and to build a decision-support tool for evaluating the statistical and ethical merits of vaccine efficacy trial designs during emerging epidemics. This work will inform HIV control policies and prepare for vaccine research during future outbreaks of emerging pathogens like the West African Ebola epidemic. While spanning diverse questions, these goals are united by their innovative integration of classically distinct fields: mathematical modeling and epidemiological study design. Aim 1: The HIV transmission rate has been measured almost exclusively in cohorts that follow stable partnerships between infected and uninfected partners (serodiscordant couples). Yet, couples with a high propensity to transmit exhibit serodiscordance fleetingly, reducing their representation in such studies and downwards-biasing estimates of the HIV transmission rate. To characterize heterogeneity in HIV transmission, adjust for its role in biasing transmission rate estimates, and assess its impact on HIV control strategies, this research will fit a couples transmission model to a 20-year long population cohort data set from Rakai, Uganda that superimposes a model of the cohort's study design over transmission, couple formation and dissolution, loss-to-follow up, and mortality processes. Aim 2: HIV epidemic severity varies widely at both national and subnational levels in sub-Saharan Africa. Limited understanding of the relative role of biological and behavioral drivers underlying this variation hampers the development of successful and locally tailored control strategies. This work will use observed couple serostatus distributions from Demographic and Health Surveys in 25 African countries and counterfactual simulations to systematically partition out the extent to which elevated transmission rates vs. riskier sexual mixing behaviors drive the most severe epidemics and to inform locally-tailored control strategies. Aim 3: Debates on the ethical and statistical merits of diverse trial designs contributed to the delayed initiation of Ebola vaccine trials until after the epidemic had substantially declined. To prepare more rapid decision-making capabilities for future epidemics of acute emerging pathogens, this work will develop a simulation-based decision-support tool that crystallizes ethical and statistical tradeoffs between diverse trial designs and facilitates interdisciplinary dialogue between clinicians, epidemiologists, modelers and bioethicists.