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Rebels in a pandemic

Speed read
  • The coronavirus pandemic has prompted government restrictions on movement
  • As time wore on, some citizens began to defy stay-at-home orders
  • Timing and information may affect social distancing more than regulations

Breaking the rules can be fun. We all remember the adrenaline spike of sneaking out past curfew or pushing the accelerator over the speed limit. 

Sadly, being transgressive often has consequences.

<strong>Do government orders determine</strong> whether people choose to stay safe at home? Or is some other factor more important?Right now, we’re living with the consequences of actions taken to curb COVID-19. We’re also living with the decisions made by a few Americans to flaunt stay-at-home orders designed to flatten the curve of infection.

The psychology behind why some individuals choose to defy measures meant to protect public health intrigues scientists like Kosali Simon and Sumedha Gupta of Indiana University. They don’t want to judge the rebels: they want to understand them.

And to do that, they’re examining phone location data to see who was breaking orders to shelter in place—and when.

Looking back to move forward

One of the surprising revelations of this study is that the various state-issued stay-at-home orders didn’t have as great an impact on behavior as earlier actions. Specifically, Simon explains that the national emergency declaration on March 13 had the biggest single impact on an average citizen’s decision to stay home.

“That weekend is when there was this massive drop off in movement,” says Simon. “There were changes since then, but much more gradual than the initial big drop off.”

<strong>A national emergency declaration</strong> on March 13 had the biggest single impact on mobility in the US. Courtesy dmbosstone. <a href='https://creativecommons.org/licenses/by-nc-nd/2.0/'>(CC BY-NC-ND 2.0)</a>The researchers have various theories about why the first emergency declaration affected mobility more than eventual stay-at-home orders. Chief among them is the simple fact that people became fatigued with staying indoors as time wore on.

The second theory is that the emergency declaration was an informative piece of news, while the stay-at-home orders placed legal restrictions on mobility. People’s beliefs about the risks they face play a large role in their actions, Simon says. What matters is when state policies occurred relative to when people’s guard went up from other means.

“The emergency declaration was a very early sense of government action, which perhaps was more about conveying information, “says Simon. “It was at the time when people were just starting to hear the other information at the national level.” 

Private data, public use

This study relied heavily on near-real-time cell signal data, recently released by Google and Facebook. Though the data was sourced only from mobile users who have opted in to location tracking, security remained a major priority.

<strong>Real-time cell signal data</strong> allows researchers to analyze how many people are moving around—and how much social mixing occurs at a specific location.The location data the researchers accessed was anonymized and aggregated at the county block level, which contains between 600 and 3,000 people on average. Companies like Google and Facebook already store location information and sell it to marketers. They are now releasing it to help researchers understand trends during the COVID-19 pandemic. 

“Those companies have now, for the public good, made these data available to researchers for free,” says Simon. “Some of the data, which is more granular, you have to sign a data use agreement for.”

But data alone is not enough. To get a better understanding of what was happening in real time, the researchers needed supercomputing resources to compile and analyze the mobility information from nearly 22 million devices. In this case, they turned to Indiana University’s large-memory computer cluster for data-intensive computing.  

“Specifically, we use Carbonate to do the API downloading of the SafeGraph's data for the first step of just being able to get it,” says Simon. “I use a supercomputing system for a lot of the initial cleaning work, because before we aggregate it, files can be larger. Once the files are more compressed, we used a secure computing system to analyze for the modules we were fitting to get our results.” 

<strong>COVID-19 is going to be with us for a while.</strong> Large-scale analysis of movement patterns can help scientists figure out which decisions most effectively lead to social distancing. Additional data and services from PlaceIQ helped measure how much social mixing occurred at a specific location. Point-of-interest data from SafeGraph helped uncover the median time spent at home.

“We are hoping this provides a foundation for ourselves and for other researchers to know exactly how we can use this data, what is it telling us, and then study many of the second-order questions,” says Gupta. “We want to know if these decisions are leading to social distancing, and how we measure that.” 

Don’t forget to humanize

It’s easy to criticize those defying stay-at-home orders. But people make choices based on the options they have before them. Perhaps the stay-at-home orders should have been issued sooner, so people wouldn’t have been fatigued by the news and would have initially followed restrictions more closely.

In the future, governments might focus more on informing people quickly, so they can make decisions for themselves. Research like this study can help societies understand how to improve their response in the case of future emergencies.

In the meantime, the study of COVID-19 responses will encompass many areas of scientific inquiry, and will continue to be analyzed for years to come. 

“As we start to reopen the economy, our team and the whole world of researchers are looking at the economic consequences of this epidemic,” says Gupta. “We’re wondering how people are going to respond.”

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