QinQin is a researcher in the Section of Viral Genomics at Statens Serum Institut in Denmark, where she develops methods for viral genomic surveillance and uses linked genomic and registry data to study viral transmission and evolution. A physicist by training, she holds a PhD in biophysics from UC Berkeley and was a postdoctoral researcher at the Harvard T. H. Chan School of Public Health.
Abstract: Genomics tools can improve our understanding of pathogen transmission and evolution. However, large dataset sizes and sampling biases can be a challenge for conventional methods. In this talk, I will discuss potential biases in the subset of the population captured by wastewater surveillance, share a new method for estimating pathogen effective population size using genomics data that accounts for sampling bias and has the potential to be higher throughput, and introduce ongoing work leveraging linked genomics and population registry data from Denmark to characterize SARS-CoV-2 transmission dynamics in day care settings.