UC Irvine

A central task in statistical analyses of infectious disease surveillance data is nowcasting transmission dynamics, understanding how transmissible a pathogen is in the present day. One way to summarize transmissibility is through the effective reproduction number, the average number of individuals an individual infected today would subsequently infect under current conditions. When the effective reproduction number is above one, an outbreak is expected to grow, the reverse is true when it is below one. Estimating the effective reproduction number from observed data is non-trivial, as epidemics are only ever partially observed, and existing data streams are subject to ascertainment biases that must be taken into account. Ideally, epidemics would be modeled as a partially observed stochastic process, but in practice this is computationally prohibitive. In this dissertation, we develop statistical models for estimating the effective reproduction number from a variety of data sources using a series of computationally tractable approximate models of epidemics. In particular, we develop models for estimating the effective reproduction number from case and test data, from pathogen genome concentrations collected from wastewater in large populations, and pathogen genome concentrations collected from wastewater in small populations. We compare our methods against state-of-the-art methods in simulation studies, and apply our methods to estimate the effective reproduction number of SARS-CoV-2 in California from 2020 to 2022.