This document discusses the role of data science during and after the COVID-19 pandemic. It covers how data science can help with virology, genomics, drug discovery, vaccine development, diagnosis modeling, testing, contact tracing, and understanding the spread of the virus. It also discusses how data science can aid other areas impacted by COVID-19 like supply chains, education, manufacturing and more. Finally, it provides some lessons for data science including understanding limitations of data and models, working with experts, and creating accurate visualizations.

1. Philip E. Bourne, PhD, FACMI
peb6a@virginia.edu
http://www.slideshare.net/pebourne
April 20, 2020
Data Science During and
After COVID-19
A Fireside Chat

2. Data Science Meets COVID-19 – Health
Virology
Genomics
Molecular Biology
Drug Discovery
Drug Repurposing
Vaccine Development
Diagnosis Modeling
Spread
Resources
Contact
Tracing
Testing
Virus
Antibodies
Mutation Rate
Pathogenicity
Virulence
DD - Years
DR - Months
VD – 12-18 months
Disposition Ethical Concerns
Confounders
Methods
Unknown Denominator
Availability
Reliability

3. Data Science Meets COVID-19 – Other
Supply Chains FraudHealth Disparities
Online Learning
Educational Analytics
Health Knowledge Sharing
Language Diversity
Education Economics Manufacturing Finance IT
Cybersecurity
Smart
Cities
Environment
Transport

4. Lessons Relevant to Data Science
• All models are wrong but some are useful – George Box
• Public health systems that are local, accessible, accurate and
complete are lacking – we cant estimate the denominator now and
may never get an accurate estimate
• We have a duty to explain the limitations in the data
• Work with experts in epidemics – e.g., CORD-19
• Accurate and meaningful visualization is critical

5. References
• Coronavirus Research Center, Johns Hopkins University & Medicine, https://coronavirus.jhu.edu/map.html
• Epidemic Calculator, http://gabgoh.github.io/COVID/index.html
• Fast Company, https://www.fastcompany.com/90476143/the-story-behind-flatten-the-curve-the-defining-chart-of-the-coronavirus
• Government of Iceland, https://www.government.is/news/article/2020/03/15/Large-scale-testing-of-general-population-in-Iceland-underway/
• Imperial College Longon, https://spiral.imperial.ac.uk:8443/handle/10044/1/77482
• Information Is Beautiful, https://informationisbeautiful.net/visualizations/covid-19-coronavirus-infographic-datapack/
• Kaggle, https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
• Medium, https://medium.com/@tomaspueyo/coronavirus-the-hammer-and-the-dance-be9337092b56
• New England Complex Systems Institute, https://necsi.edu/review-of-ferguson-et-al-impact-of-non-pharmaceutical-interventions
• Nightingale, https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8
• STAT, https://www.statnews.com/2020/03/10/simple-math-alarming-answers-covid-19/
• World Health Organization, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
• NIH https://datascience.nih.gov/
• The COVOD-19 High Performance Computing Consortium https://covid19-hpc-consortium.org/
• CORD-19 Open reserch Literature Dataset https://pages.semanticscholar.org/coronavirus-research
• Biocomplexity Instutute https://covid19.biocomplexity.virginia.edu/