UC Berkeley

This dissertation offers an examination of three different infectious diseases, in each case exploring and evaluating a method for estimating a variable related to disease burden that is inherently difficult to measure. In each instance, the variable has critical implications for study findings or public health interventions, and the typical treatment of this variable in those settings is known to involve considerable information bias.

Chapter 1 provides an overview of the analyses presented here, along with the scientific contributions of the work, individually and as a cohesive set of studies.

Chapter 2 explores a new method for estimating dates of initial infection for people living with HIV. This analysis explains the way in which this new method for estimating infection dates substantially improves results over the conventional Fiebig staging method currently in wide use. The major benefit of this new method is that it allows for estimation of a date of detectable infection (with an associated confidence interval) for any person who has at least one positive and one prior negative HIV test of any kind; the tests do not need to be run on the same day, and need not be run during the acute phase of infection, unlike the commonly used Fiebig method.

Chapter 3 is an analysis of the feasibility and effectiveness of daily temperature monitoring as a predictor of infection with SARS-CoV-2, the virus that causes COVID-19. A practice commonly applied in airports, at hotel doorways, and in school entryways around the world, temperature screening is non-invasive and reassures an anxious public during a deadly disease outbreak like SARS, Ebola, or COVID-19. Findings from the Berkeley COVID-19 Safe Campus Initiative were that daily temperature monitoring was feasible and acceptable; however, the majority of potentially infectious individuals were not detected by temperature monitoring, suggesting that temperature screening is insufficient as a means of detection to reduce transmission of SARS-CoV-2.

Chapter 4, details the methods and findings of an analysis to estimate a "cascade of care" for hepatitis C virus (HCV) testing and treatment among three key subgroups in San Francisco that bear a heavy burden of HCV disease: people age 30 and under who inject drugs; men who have sex with men and inject drugs; and trans women. Public healthprofessionals commonly use cascades of care to estimate the number of cases at each stage of diagnosis and care of an infection, providing essential baseline information for developing and evaluating interventions; however, these types of cascades undercount cases among many "hidden populations" that are medically underserved, limiting their effectiveness for understanding the nuances of a local epidemic. This analysis incorporates data from published literature, clinical health records, and cross-sectional and cohort studies in San Francisco to estimate the population size and proportion of people in each HCV cascade stage for these subgroups, providing a foundation for the development of roadmaps to HCV elimination.

Chapter 5 summarizes the conclusions drawn from this work and outlines a series of next steps that will further advance the field in each case.