Utilizing genomic data for reconstructing tuberculosis transmission: advances and challenges

Establishing patterns of tuberculosis (TB) transmission - who infected whom - is critical for controlling ongoing outbreaks and informing future strategies to eliminate the disease. However, reconstructing transmission networks of infections caused by Mycobacterium tuberculosis (MTB)​can be difficult due to a highly variable latency period in which within-host evolution can occur and the clonal nature of MTB, particularly in low-incidence regions. Here, I present an overview of the current strategies being used to exploit genomic variation to resolve transmission networks, and introduce work that we are doing at the BCCDC to understand TB transmission dynamics in BC. Bio: Dr. Sobkowiak is a Postdoctoral Research Fellow at BCCDC working on developing risk prediction models for TB transmission in BC through the application of bioinformatics and machine learning to genomic, epidemiological and healthcare data. He completed his PhD in Computational Biology in 2017 under Prof. Francois Balloux at University College London, UK and undertook his first postdoctoral position with Prof. Taane Clark at the London School of Hygiene and Tropical Medicine, UK, investigating TB transmission dynamics and associated risk factors in Malawi.

BC CDC Presenters

2/18/2020 8:00:00 PM