A Seat at the Table: The Key Role of Biostatistics and Data Science in the Pandemic
About this Webinar Series
The COPSS-NISS COVID-19 Data Science webinar series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) and its five charter member societies (ASA, ENAR, IMS, SSC, and WNAR), as well as NISS. This bi-weekly seminar features the latest research that is positioned on the cusp of new understanding and analysis of COVID-19 pandemic data, and promotes data-driven research and decision making to combat COVID-19. Find out more about this series and view all the previous sessions on the Webinar Series 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 realizing 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-data science 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.
Jeffrey Morris, (University of Pennsylvania Perelman School of Medicine)
Alisa J. Stephens-Shields, (University of Pennsylvania Perelman School of Medicine)
Xihong Lin, (Harvard)
Lingzhou Xue, (Penn State)