Department Seminar by Grace Yi, Department Statistical and Actuarial Sciences, Department of Computer Science University of Western Ontario

Thursday, June 4, 2020 4:00 pm - 4:00 pm EDT (GMT -04:00)

Can the reported COVID-19 data tell us the truth? Scrutinizing the data from the measurement error models perspective

The mystery of the coronavirus disease 2019 (COVID-19) and the lack of effective treatment for COVID-19 have presented a strikingly negative impact on public health. While research on COVID-19 has been ramping up rapidly, a very important yet overlooked challenge is on the quality and unique features of COVID-19 data. The manifestations of COVID-19 are not yet well understood.  The swift spread of the virus is largely attributed to its stealthy transmissions in which infected patients may be asymptomatic or exhibit only flu-like symptoms in the early stage. Due to the limited test resources and a good portion of asymptomatic infections, the confirmed cases are typically under-reported, error-contaminated, and involved with substantial noise. If the drastic effects of faulty data are not being addressed, analysis results of the COVID-19 data can be seriously biased.

In this talk, I will discuss the issues induced from faulty COVID-19 data and how they may challenge inferential procedures. I will describe a strategy of employing measurement error models to address the error effects. Sensitivity analyses will be conducted to quantify the impact of faulty data for different scenarios.  In addition, I will present a website of COVID-19 Canada (, developed by the team co-led by Dr. Wenqing He and myself, which provides comprehensive and real-time visualization of the Canadian COVID-19 data.

Please note: This seminar will be given online through Webex. To join, please follow this link: Virtual seminar by Grace Yi.