Webinar Series: Data Science in Action in Response to the Outbreak of COVID19

A statistical transmission model for COVID-19 outbreak, with adjustment of external factors

Speaker:

Dr. Yifan Zhu, Staff Scientist, Fred Hutchinson Cancer Research Center

Abstract:

The COVID-19 pandemic showed some resemblances to previous outbreaks caused by coronaviruses such as SARS and MERS. However, by combing features such as relatively high transmission rate, potential of asymptomatic/pre-symptomatic infection and high case fatality rate among the vulnerable, this virus has presented significant challenges to public health systems. In this talk, I will introduce a statistical model for inferencing the transmission dynamics of COVID-19 outbreak incorporating some special features of the virus, and the approaches to combine certain spatial, temporal, social and demographic factors into the model. Early analytical results applied to the observed epi-curves in Wuhan, China enabled us to understand the outbreak monitoring data in other regions. We will present some preliminary inferences for the ongoing COVID-19 outbreak data from several US states.

Bio:

Dr. Yifan Zhu is a staff scientist from the Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center. He received PhD in Biostatistics from University of Florida in 2015 before joining the Hutch. Dr. Zhu has worked on various projects on design and inference of HIV prevention/vaccine trials, infectious disease transmission dynamic models, physical activity monitoring studies, as well as methodological research such as Bayesian model selection, functional data analysis, and compositional data analysis.