UC Irvine

Statistical modeling of infectious disease data is among the oldest applications of statistics. Today, it is an increasingly relevant application of research, due to globalization that enables diseases to spread further and faster, as well as the abundance of relevant data from electronic surveillance systems, seroprevalence studies, and genetic sequencing of pathogens. In this work, we develop novel statistical methods to combine varied data sources to improve both inference and forecasting. First, we work with data from assay validation studies and active surveillance studies to develop confidence intervals for prevalence estimates from complex surveys with imperfect assays. In this complicated setting, there are no established competitive methods, and ours exhibits at least nominal coverage. In addition, we apply our model in simplified cases where competitors exist and demonstrate desirable properties. Next, we develop a semi-parametric Bayesian compartmental model that effectively integrates passively collected time series of diagnostic tests and mortality data, as well as actively collected seroprevalence data. We emphasize retrospective inference and evaluate the utility of each data stream in the context of short-term forecasting. Finally, we focus on healthcare demand forecasting during epidemic surges of pathogen variants capable of immune escape. We build upon our Bayesian compartmental model to incorporate time series of cases, hospitalizations, ICU admissions, deaths, and genetic sequence counts. We show that using genetic information leads to superior forecasting performance, compared to traditional models. Throughout each project, we employ our methods to analyze a variety of COVID-19 data sets at the county, state, and national levels.