Because public health data is being collected from so many different sources, it can be difficult to accurately quantify the scale of the pandemic.
Wharton Statistics Professor Adi Wyner, faculty lead of the Wharton Sports Analytics and Business Initiative explains the nuances of coronavirus data and what insights can come from those numbers.
“Part of the problem with the data is that it’s fundamentally unreliable, but not all unreliability is created equal—state testing procedures are very different,” explains Wyner. “Because we aggregate over all the States and you have these incredibly different testing protocols, it becomes fundamentally a question of, ‘What can you infer from the data?’ On the other hand, certain states have had fairly consistent testing protocols, and then you can really see what’s happening as you march across time.
“Yes, there’s unreliability, but there’s also an ability to do something with the data even though there are problems with it because those problems, at least, are consistent. If you’re looking for the bend in the curve, or the ‘flattening of the curve,’ as long as those problems are consistent over time, you can still measure the bend. If it’s inconsistent, well, then almost all bets are off.”
This article is by Emily O’Donnell. Read more at Wharton Stories.
