What We Learned Statistically Through COVID-19 Pandemic-Related Research

• Activity Number: 479 - What We Learned Statistically Through COVID-19 Pandemic-Related Research
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Bhramar Mukherjee* and Dean Follmann* and Alex Luedtke* and Jeffrey Morris* and Usha Govindarajulu*
• Companies: University of Michigan and National Institute of Allergy and Infectious Diseases and University of Washington and University of Pennsylvania and Icahn School of Medicine at Mount Sinai
• Keywords: COVID-19; data analysis; data science; statistics; vaccine evaluation; vaccine efficacy

Abstract

Panelists: Usha Govindarajulu, Bhramar Mukerjee, Dean Follmann, Alex Luedtke, and Jeffrey Morris

The purpose of this topic-contributed proposal is to have a panel discussion from various statistical researchers who have done in depth COVID-19 related research or who have been a voice for best statistical practices in this area of research since the beginning of the pandemic in the year 2020. Each panelist will present or discuss how various strategies of statistical and/or data science methods that were utilized in COVID-19 research through their own research or other research that has been conducted, then a discussant will summarize and present their topics and also current issues, and then finally blend into a nice panel discussion in order to discuss the strength and weaknesses of strategies that were employed. Presenter topics will range from lessons learned from the COVID-19 data driven framework in the U.S. and also in India to vaccine evaluation and efficacy trials to the confusion by the general public with all the presentation of the data. It shall be an exciting panel discussion on these current challenges in data analytic strategies during the COVID-19 pandemic.