A seat at the table: The key role of biostatistics and data science in the COVID-19 pandemic
Dr. Morris is a Professor and Director of the Division of Biostatistics at the Perelman School of Medicine at the University of Pennsylvania. He is a Fellow of the Institute for Mathematical Statistics (IMS) and of the American Statistical Association (ASA), and has been recognized with numerous national honors, including the ASA’s Noether Young Scholar Award and Harvard University’s Myrto Leftkopoulou Distinguished Invited Lectureship. He has served as President of the East North American Region (ENAR) of the International Biometric Society and overall program chair for the Joint Statistical Meetings, and is also currently the editor of Biology, Medicine and Genomics for the IMS journal The Annals of Applied Statistics.
Dr. Morris’ research interests focus on the development of quantitative methods to extract knowledge from biomedical big data, including work to relate complex biomedical object data to patient outcomes using flexible, automated regression methods, and to integrate information across multiple types of multi-platform genomic, proteomic, imaging, and wearable device data to uncover biomedical insights contained in these complex data that might be missed by more simplistic approaches. He has also done extensive applied work in cancer research, including constructing novel prognostic indices for hepatocellular carcinoma and helping develop and characterise molecular subtypes of colorectal cancer for precision therapeutic strategies.
During the pandemic, he has been involved on a number of COVID-19 related research projects involving serology, immunology, and modeling the pandemic, plus co-authored a review article for Statistics in Medicine comparing statistical contributions for the AIDS and COVID-19 pandemic. He also authors a blog https://covid-datascience.com in which he attempts to use his skills and perspectives as a statistical data scientist to evaluate and synthesise accruing information in the pandemic, debunking misinformation and filtering out political and other sources of bias, to clearly communicate objective, empirically-based knowledge about various aspects of the pandemic to the general audience, and has been active in speaking with television, print, and online media about emerging scientific knowledge during the pandemic.
For more information on Dr Jeffrey Morris’ publicaitons, please visit his Google Scholar page.
The novel virus SARS-CoV-2 has produced a global pandemic, forcing doctors and policymakers to “fly blind,” trying to deal with a virus and disease they knew virtually nothing about. Sorting through the information in real time has been a daunting process—processing data, media reports, commentaries, and research articles. In the USA this is exacerbated by an ideologically divided society that has difficulty with mutual trust, or even agreement on common facts. The skills underlying our statistical profession are central to this knowledge discovery process, filtering out biases, aggregating disparate data sources together, dealing with measurement error and missing data, identifying key insights while quantifying the uncertainty in these insights, and then communicating the results in an accessible balanced way. As a result, we have had a central role to play in society to bring our perspective and expertise to bear on the pandemic to help ensure knowledge is efficiently discovered and put into practice. Unfortunately, our profession is often shy about asserting its perspective in broader societal ventures, perhaps not realising the central importance of our perspective and mindset. I have authored a website and blog covid-datascience.com that represents my own person efforts to disseminate information I have found reliable and insightful regarding the pandemic, accounting for subtle scientific and data analytical issues and uncertainties about our current knowledge, and seeking to filter out political and other subjective biases.
Using experiences with the covid-datascience blog as a backdrop, I will highlight how statistical and data scientific issues have been central in understanding the emerging knowledge in the pandemic. I will discuss various broad issues I have seen impede the knowledge discovery process, including subjective bias causing individuals to ignore some information and magnify others, viral misinformation spread on social media platforms, danger of rushed and inadequately reviewed scientific studies, conflating of political concerns and scientific messaging, and incomplete and messaging from scientific leaders to the broader community. I will discuss these concepts in various specific contexts, including identification of key modes of spread and effective mitigation strategies, vaccine safety and efficacy, durability of immune protection and risk of reinfections or breakthrough infections, and the emergence of variants of concern and how this affects the pandemic moving forward. I will also highlight efforts of COVID-Lab team that has used hybrid statistical-epidemiological model to model county-level data throughout the pandemic, successfully identifying areas at risk of surge and heavily used by regional, state, and national leaders to manage the pandemic. I will finish with a call to urge statisticians to seek greater visibility and engagement with the media and policymakers to ensure our understanding of quantitative nuances is reflected in important societal-level decisions and dissemination of emerging scientific knowledge.