Tuberculosis (TB) is the leading cause of death due to infectious disease globally, yet our understanding of its transmission patterns and incidence are limited. With the WHO's goal to eliminate TB by 2030, tools to monitor progression toward this goal are needed. In this talk, Dr. White will discuss work to estimate transmission patterns of TB using routinely collected data, as well as data from commonly conducted epidemiological studies. She will focus on estimation of the serial interval, which has not been studied in TB. Using a cure model and interval censoring techniques, estimates of the serial interval have been developed that can inform modeling studies and public health practice in TB control. She will also show how routinely collected surveillance data and estimates of the serial interval can be used to generate estimates of the reproductive number. These estimates can be created across heterogeneous groups to reveal areas where transmission is occurring most, allowing for more focused allocation of resources. She will describe an approach for understanding the transmission tree, using routinely collected surveillance data and limited genetic information. This method allows them to infer the reproductive number in the absence of a reliable estimate of the serial interval and better understand pairwise transmission probabilities.