MIT Sloan Management Review Article on The Challenges of Presenting Pandemic Data

A pandemic demands that leaders make informed judgments about when to close, reopen, and, when necessary, reclose struggling economies. Managers must grapple with decisions such as whether they should bring workers back onsite, resume business travel, and welcome the return of retail shoppers. Although the success of any policy ultimately hinges upon whether individuals adhere to these protocols, the potential toll of a virus on employees, customers, and businesses means that accurate forecasting is essential.

Forecasting guides planning, and forecasts rely on data. Modern pandemic data is inherently a time series of points that represent, say, the unfolding number of cases over time. This means that when time is presented in a graph, it must always sit squarely on the x-axis, but there is leeway in deciding what variable runs up the y-axis. Could the takeaways from the data differ depending on this subtle framing choice?

About the Author

Sam Maglio is an associate professor of marketing and psychology at the University of Toronto Scarborough and the Rotman School of Management.

Nicholas Reinholtz is an assistant professor of marketing at the University of Colorado Boulder.

Stephen Spiller is an associate professor of marketing and behavioral decision-making at the UCLA Anderson School of Management.

Learn more about MIT SMR.