A Biostatistician's Encounter with COVID-19 in New York City
Usha Govindarajulu (Icahn School of Medicine at Mount Sinai)
In this presentation, I will discuss how working at a major New York City hospital system in the epicenter of the COVID-19 pandemic has changed my life forever. Around late March, I was pulled into COVID research for the hospital like I have never seen before. Suddenly being redeployed to work with a team of people who I had never met, who were also redeployed was a new challenge as well as keeping up with the daily download of updated data and analysis requests from infectious disease specialists to anesthesiologists. Dealing with real-time data analysis suddenly became the new normal. Handling messy data and constantly changing focus have been issues throughout. The challenge became being able to make meaning from all of this observational data. We certainly had more than enough patients but we needed to make correct interpretations of the messy and potentially biased data with meaningful statistical methods. Meanwhile there was institutional pressure to get the results fast. The pressure is still there and it is real as people are racing to find a cure for this horrible virus. Please hear how I have tried to manage during this crisis with making meaningful results out of data driven, time sensitive analyses.
Usha Govindarajulu is a Senior Faculty in the Center for Biostatistics in the Department of Population Health Sciences of the Icahn School of Medicine at Mount Sinai. She graduated from Boston University with a PhD in Biostatistics and spent two years as a postdoctoral fellow at Harvard School of Public Health. She then worked for a year research faculty at Yale University before moving back to Boston and working at Brigham & Women’s and Harvard Medical School. After being there about 5 years, she moved to New York and took as a position as an Assistant Professor of Biostatistics at SUNY Downstate School of Public Health. She was there approximately 7 years before leaving to be in her current position. Her research interests are in survival analysis, frailty models, causal inference, genetic epidemiology, and machine learning. She is the 2020 Chair-Elect of the Section on Statistical Computing of the American Statistical Association.