During last week’s ODSC East 2020 Virtual Conference, we hosted a number of sessions on the most important topic of today – COVID-19, aka Coronavirus. These talks showed how data science and AI can be used in the realm of pandemics, ranging from diagnosis and spreading, to modeling preventative measures to avoid future pandemics. Considering the importance of this topic, we’ve decided to keep all of these sessions available for viewing for free. Below is a list of all of the ODSC East 2020 COVID-19 track sessions that you can now watch on-demand anytime you wish.
[Related article: Coronavirus Data Science Research Papers to Read Right Now]
CEDAR: Information Technology to Enhance Open Science in the Fight Against COVID-19: Mark Musen, PhD, Professor & Director | Stanford University & Stanford Center for Biomedical Informatics Research
In collaboration with the GO-FAIR International Support and Coordination Office, CEDAR is participating in the Virus Outbreak Data Network to develop robust approaches to the sharing of scientific datasets related to COVID-19.
COVID-19: Unprecedented Challenges and Opportunities for Data Science (and Scientists) — Voices, Visions, and Ventures form Harvard Data Science Review: Xiao-Li Meng, PhD, Professor & Founding Editor-in-Chief | Harvard University & Harvard Data Science Review
Coronavirus After the Curve: Roger W. Thomas, Senior Director, Growth & Strategy for Manufacturing & Diversified Industries | Oracle
While today’s focus is rightfully spent on flattening the curve, those in good health must start to ask what happens after the curve. This question is one for which data science can offer a unique perspective. In this talk, Roger related his Do’s & Don’ts experience on manufacturing analytics to reflect on the role data scientists can play in shaping the economic recovery so desperately needed.
How Can a Democracy Effectively Respond to COVID-19: Lessons from Taiwan: Jason Wang, PhD, Director of Center for Policy, Outcomes and Prevention | Stanford University
In January and February of 2020, Taiwan has rapidly produced and implemented a list of at least 124 action items within 5 weeks (3 to 4 per day) to protect public health. Stopping infections requires case identification and monitoring of close contacts; Taiwan has used new integrated data and technology to accomplish that. This has resulted in one of the lowest infection rates and case fatality rates in the world so far.
Tracking Undetected COVID-19 Infections Using Coronavirus Genomes: Lucy Li, PhD, Infectious Data Scientist | Chan Zuckerberg Biohub
In many places, increasing testing rates is not an option due to resource constraints. However, we can get an estimate of the number of undetected infections therefore requires inference using mathematical models. One particular approach focused in on in this session is the use of phylodynamic models of infectious disease transmission, which describe not only how many infections we expect over time but also the expected changes in the viral genome.
AI for COVID-19: Developing the “Corona-Score” for patient monitoring using Deep Learning CT Image Analysis: Hayit Greenspan, PhD, Head of Medical image Processing Lab | Tel-Aviv University
In this talk, Hayit showed AI-based automated CT image analysis tools that show promise in supporting the detection of COVID-19 manifestations as well as in the quantification of disease burden. As such they need to be considered as a key tool in the diagnosis, and monitoring of CVID-19 patients.
Other speakers include:
- Johannes Eichstaedt, PhD, Computational Social Scientist & Junior Research Fellow | Stanford University
- Fawad Butt, Chief Data & Analytics Officer | United Healthcare & Optum
- Belinda Seto, PhD, Deputy Director | National Institutes of Health
- Susan Gregurick, PhD, Director | National Institutes of Health
- Dr. Eric Siegel Professor, Vice Chair | University of Maryland School of Medicine
Be sure to check out all of these talks here for free and remember to share them with your colleagues in data science and AI!