Seven Principles for Rapid-Response Data Science: Lessons Learned from Covid-19 Forecasting

TL;DR

This paper distills seven key principles learned from a rapid-response data science team during Covid-19, emphasizing agile methods, domain knowledge, and quick deployment for effective forecasting and resource distribution.

Contribution

It presents a set of seven principles specifically tailored for rapid-response data science in emergency situations, based on real-world Covid-19 forecasting experience.

Findings

Effective rapid-response data science requires domain expertise and agile practices.

Curated data repositories and automated visualization tools enhance decision-making.

Applying these principles improved Covid-19 forecasting and resource allocation.

Abstract

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.