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 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.
Cite
@article{arxiv.2108.08445,
title = {Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting},
author = {Bin Yu and Chandan Singh},
journal= {arXiv preprint arXiv:2108.08445},
year = {2022}
}
Comments
4 pages, accepted in special issue of "Statistical Science" on COVID-19 Response