Open Access
May 2022 Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting
Bin Yu, Chandan Singh
Author Affiliations +
Statist. Sci. 37(2): 266-269 (May 2022). DOI: 10.1214/22-STS855


In this article, we take a step back to distill seven principles out of our experience in the spring of 2020, when our 12-person rapid-response team used skills of data science and beyond to help distribute 340,000+ units of Covid PPE. This process included tapping into domain knowledge of epidemiology and medical logistics chains, curating a relevant data repository, developing models for short-term county-level death forecasting in the US, and building a website for sharing visualization (an automated AI machine). The principles are described in the context of working with Response4Life, a then-new nonprofit organization, to illustrate their necessity. Many of these principles overlap with those in standard data-science teams, but an emphasis is put on dealing with problems that require rapid response, often resembling agile software development. The technical work from this rapid response project resulted in a paper (Altieri et al. (2021)); see also this interview for more background (Yu and Meng (2021)).

Funding Statement

Partial support of a grant from The Center for Information Technology Research in the Interest of Society and the Banatao Institute (CITRIS), University of California, is gratefully acknowledged.


Thanks to Tiffany Tang, Hue Wang, Nick Altieri and Xiao Li for their helpful input to the paper, as well as two referees for their constructive comments that improved the paper.


Download Citation

Bin Yu. Chandan Singh. "Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting." Statist. Sci. 37 (2) 266 - 269, May 2022.


Published: May 2022
First available in Project Euclid: 16 May 2022

MathSciNet: MR4422308
zbMATH: 07535203
Digital Object Identifier: 10.1214/22-STS855

Keywords: Coronavirus , county-level , data-science , forecasting

Rights: Copyright © 2022 Institute of Mathematical Statistics

Vol.37 • No. 2 • May 2022
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