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 (https://covid-19-canada.uwo.ca/), 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.
About the Speaker
Grace Y. Yi is a professor of the Department Statistical and Actuarial Sciences and the Department of Computer Science at the University of Western Ontario where she currently holds a Tier I Canada Research Chair in Data Science. Dr. Yi's research interests focus on developing methodology to address challenges concerning measurement error, causal inference, imaging data, missing data, high dimensional data, survival data, and longitudinal data. She authored the manuscript “Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application” (2017, Springer).
Dr. Yi received her Ph.D. in Statistics from the University of Toronto in 2000 and then joined the University of Waterloo as a postdoctoral fellow (2000-2001), Assistant Professor (2001-2004), Associate Professor (2004-2010), Professor (2010-2019), and University Research Chair (2011-2018). She is a Fellow of the Institute of Mathematical Statistics, Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. In 2010 Dr. Yi received the Centre de Recherches Mathmatiques and the Statistical Society of Canada (CRM-SSC) Prize which recognizes a statistical scientist's excellence and accomplishments in research during the first fifteen years after earning their doctorate. She is a recipient of the University Faculty Award (2004-2009) granted by the Natural Sciences and Engineering Research Council of Canada. Dr. Yi’s work with Xianming Tan and Runze Li won The Canadian Journal of Statistics Award in 2016.
Dr. Yi has served the professions in various capacities. She was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018), the President of the Biostatistics Section of the Statistical Society of Canada in 2016, and the Founder of the first chapter (Canada Chapter, established in 2012) of International Chinese Statistical Association. She will take on the Presidency of the Statistical Society of Canada for the period of 2020-2022.