Biostatistics Research Day is an annual departmental event that showcases student research and promotes interdisciplinary research among graduate students and faculty in Public Health.
This year's speaker is James Dignam, PhD (BIOST '94).
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
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.
Last Updated On Friday, February 15, 2019 by Crow, Sharon Weber
Created On Friday, February 15, 2019
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