Mobility tracker gives Wisconsin researchers better understanding of COVID-19 spread, travel patterns

LA CROSSE, Wis. (WKBT)– UW-Madison researchers are using cell phone data to see how people are changing their travel habits in response to COVID-19.  They’re compiling anonymous data from various vendors to also see how it may correlate with the spread of the coronavirus.

As a UW-Madison assistant professor in the geography department, Song Gao studies human mobility patterns using everything from social media to mobile phone and GPS data.

“To understand human behaviors as well as the social consequences,” said Gao, via Zoom.

In this case, how people’s movements could impact the number of COVID-19 cases.

“As we know in the early spread, it can really tie to the travels. Otherwise, the virus can’t go everywhere,” Gao said.

After the Safer At Home Order was enacted in late-March by Wisconsin Gov. Tony Evers, they wanted to see if that had any impact, especially compared to other parts of the country.

“Different regions and maybe different groups of people in different places are going to respond differently,” Gao said.

Mapping Mobility Changes in Response to COVID-19 by GeoDS Lab @ UW-Madison.

The same goes for once the order was lifted. The areas in red show counties where travel distance is increasing and blue for where it is decreasing.

“That’s why it’s critical, perhaps now more than ever, that Wisconsinites step up and stay home,” said Gov. Evers, during a Tuesday press conference.

Evers warned residents that hospitals were nearing capacity. He said that wearing a mask alone wouldn’t flatten the curve.

“I am once again calling on Wisconsinites to hunker down and stay at home as much as possible. And, limit travel to only to essential needs,” Evers said.

The data shown on the mobility website uses aggregated data to show trends in how far people are traveling or how long they’re staying home. Using millions of data points, they can calculate the median of maximum travel distance in kilometers over time and how many hours people are at home.

It shows people did stay home in late-March and early-April. If you compare the graph to DHS data, the transmission was also low at that time. This is especially true in places like Dane County.

“So I think that Stay At Home order really helps from our analysis,” Gao said.

But Gao is cautious and said the data does not necessarily show causation, but rather they are looking at association. By using other data, you might be able to get a better picture of how interactions impact transmission.

If you want to see far people are traveling or spending time at home in your county, you can view the interactive research project here.