Event
Smith School Special Seminar: Tinglong Dai, "Overdiagnosis and Undertesting for Infectious Diseases"
Friday, September 2, 2022
10:00 a.m.
1415 Van Munching Hall
Overdiagnosis and Undertesting for Infectious Diseases
Abstract
For much of the COVID-19 pandemic, the debate over COVID-19 testing has centered on undertesting (i.e., insufficient testing capacity relative to demand). Overdiagnosis (i.e., positive diagnoses for patients with negligible viral loads) is another important but little studied systematic issue: Evidence exists that U.S. labs have adopted highly sensitive diagnosis criteria, such that up to 90% of positive diagnoses are for minuscule viral loads. Motivated by this situation, we develop a theory that explains both undertesting and overdiagnosis. We show that a lab has an incentive to inflate its diagnosis criterion, which generates more diagnosis-driven demand due to contact-tracing efforts, albeit while dampening demand from disease transmission. An inflated diagnosis criterion prompts the lab to build more testing capacity, which may not be able to fully absorb the inflated demand, resulting in undertesting. Finally, we examine a social planner’s problem of whether to mandate that the lab report the viral load along with its diagnosis, so that a physician or contact tracer can make informed triage decisions. Our results show the social planner may choose not to mandate viral-load reporting initially; this choice induces a higher testing capacity and can help reduce disease transmission. This is a joint work with Shubhranshu Singh, also of Johns Hopkins. This working paper's full-text URL is http://dx.doi.org/10.2139/ssrn.3725057.
Biography
Tinglong Dai is a Professor at the Johns Hopkins Carey Business School, with a joint faculty appointment at the Johns Hopkins School of Nursing. He serves on the leadership team of the Hopkins Business of Health Initiative and the executive committee of the Institute for Data-Intensive Engineering and Science. Since the onset of the COVID-19 pandemic, he has been quoted hundreds of times in the media, including the Associated Press, Bloomberg, CNN, Fortune, New York Times, NPR, USA Today, Wall Street Journal, and Washington Post, and has appeared on national and international TV such as CNBC, PBS NewsHour, and Sky News. In 2021, he was named one of the World's Best 40 Under 40 Business School Professors by Poets & Quants. Dr. Dai's research interests span across healthcare operations, human-AI interaction, and marketing-operations interfaces. His work has been published in leading journals such as Management Science, M&SOM, Marketing Science, and Operations Research, and has been recognized by Johns Hopkins Discovery Award, INFORMS Public Sector Operations Research Best Paper Award, POMS Best Healthcare Paper Award, and Wickham Skinner Early Career Award (runner-up). He is an Associate Editor of Management Science, M&SOM, Health Care Management Science, and Naval Research Logistics, and a Senior Editor of Production and Operations Management. He chairs the Johns Hopkins Symposium on Healthcare Operations and co-edits the Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, published by John Wiley & Sons in 2018.
