Berkeley Data Science Team Reflects on Tackling COVID Outbreaks

May 27, 2022

BIDS Faculty Affiliate Bin Yu is featured in a recent CDSS News article that recounts experiences early on in the pandemic, in the spring of 2020, when she was approached by a new nonprofit organization seeking data science expertise in its efforts to efficiently distribute personal protective equipment (PPE) to where it was needed most. Yu – professor of statistics and electrical engineering and computer sciences at UC Berkeley – quickly recruited students from her group, and in the end, the team’s predictions helped inform the shipment of at least 349,000 face shields to doctors and healthcare workers at a time when they were desperately needed. 

Yu and PhD Student Chandan Singh were recently invited to submit a paper to a special issue of Statistical Science on Data Science in a Time of Crisis: Lessons from the Pandemic. Their paper – Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting – relates the team's experiences and distills seven principles they used to tap into domain knowledge (epidemiology) and medical logistics chains, curate a relevant data repository, develop models for short-term county-level death forecasting (in the US), and build a website for sharing visualization (an automated AI machine):

  1. The decision to engage: preparedness and willingness.
  2. Effective human organization: divide and conquer.
  3. Gathering data and context: scraping, human contacts, and media reports. 
  4. Data quality control: in-house data cleaning and curation. 
  5. Speedy development and validation of many prediction algorithms. 
  6. Uncertainty: measurement and empirical validation. 
  7. Communicating results: interactive visualizations, open-source code, and a web interface. 

“Although these principles are described in the context of working with Response4Life, many of the principles overlap with those in standard data-science,” Yu said. “But in this project, we emphasized the need for a rapid response due to the fast spread of lethal COVID-19 hot spots across the country. There were no other county-level prediction models available in the U.S. until after our paper was submitted on May 16, 2020.”

Read more:
April 20, 2022  |  Bin Yu  |  BIDS News

May 16, 2020  |
Nick Altieri, Rebecca Barter, James Duncan, Raaz Dwivedi, Karl Kumbier, Xiao Li, Robert Netzorg, Briton Park, Chandan Singh, Yan Shuo Tan, Tiffany Tang, Yu Wang, Bin Yu

Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting
May 2022  |  Bin Yu, Chandan Singh  |  Statistical Science

Statistics-Computer Sciences Team Reflects on Tackling COVID Outbreaks
May 26, 2022  |  Jon Bashor  |  CDSS News

Featured Fellows

Bin Yu

Statistics, UC Berkeley
Faculty Affiliate

Rebecca Barter

BIDS Alum – Data Science Fellow