The COVID-19 crisis prompts new questions, like who is at greatest risk of exposure to this new coronavirus. For this particular question, the answer is hiding in data that already exists.
The New York Times did an analysis showing which workers are at greatest risk of exposure to the coronavirus. The vertical axis shows how frequently people in a profession are exposed to disease and infection, indexed from 0 (never) to 100 (every day). The horizontal axis shows how the proximity between these workers and other people as they do their jobs, indexed from 0 (work alone) to 100 (very close). The size of each bubble shows how many people work in that profession.
This analysis highlights the risks to professions that might readily come to mind in the fight against the virus, like nurses and paramedics. But it also shows that bus drivers are just as prone to exposure as personal care aides. This knowledge can improve decisions about interventions and protection for workers who keep cities running in a crisis.
The data that makes this possible comes from a database maintained by the Department of Labor, called O*Net. It’s intended to help employers take a data-driven approach to analysis of training needs and workforce development. But its scores on dozens of attributes for each profession suddenly have a new use–providing guidance on risk of exposure to a new virus.
Capturing the option value of data like this, first described by Ken Cukier and Viktor Mayer-Shönberger in the book Big Data, is important for companies and government agencies on a daily basis. But it’s crucial in a crisis moment where answers to your new questions can save lives.
Data Capital 