Seven principles for rapid-response data science: lessons learned from COVID-19 forecasting. (English) Zbl 07535203

Summary: 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 [N. Altieri et al., “Curating a COVID-19 data repository and forecasting county-level death counts in the United States”, Harvard Data Sci. Rev., Spec. Issue 1, 82 p. (2021; doi:10.1162/99608f92.1d4e0dae)]; see also this interview for more background [B. Yu and X.-L. Meng, “An interview with Bin Yu”, Harvard Data Sci. Rev., Spec. Issue 1, 12 p. (2021), https://hdsr.mitpress.mit.edu/pub/5pe5xcvb].


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