Pandemic Roulette: How Researchers are Weighing Socializing Risks

Have you ever wondered how risky it is to gather with nine of your closest friends during the ongoing pandemic? 

For many, sheltering in place and being unable to see friends and family has taken a toll on their mental health. A study on how quarantine affects our mental state reported that people begin to show signs of anxiety, irritability or “low mood,” insomnia, detachment or their work performance suffers. 

Socializing can be therapeutic. But is the risk worth it? 

That is, in part, what several researchers are working to determine. 

One is called the COVID-19 Event Risk Assessment Planning Tool created at Georgia Tech. It used nationwide data to create a county-level map for folks to help determine the risk for themselves.

“We wanted to help people make better decisions,” said Clio Andris, assistant professor at Georgia Tech. “And it’s difficult for people to contextualize one case in 1,000. What (does it mean) going to a grocery store, or going to a wedding, or going to school?”

The planning tool calculates how likely it is that at least one person in a group of a certain size is infected with COVID-19, tested or not, “given a certain number of circulating infections in the specified region.” 

The project is the brainchild of Joshua Weitz, who is a professor in biological sciences at Georgia Tech. Weitz came up with the model of risk and found a way to quantify the probability of someone having COVID-19 at an event. 

As part of the development team, Andris worked with six other people to create the dashboard. 

“I’m the mapper,” she said. 

She and her team pulled New York Times data on the case rate per county as well as the population per county, giving users a granular look at the data. The data are limited, not revealing the nuance of, say, an indoor versus outdoor event. 

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COVID-19 Event Risk Planning Tool

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“All events are different so it’s hard to put those parameters in a model,” Andris said.

The map takes into account that there could be either five to 10 times more cases, which they label as ascertainment bias. 

For example, getting together with 10 friends in Douglas County with an ascertainment bias of 5, there is a 6% risk level that someone in that group has the virus. In Wyandotte County, the risk level under the same conditions is 20%.

However, tack on an ascertainment bias of 10, and Douglas County’s risk level rises to 12% and Wyandotte County’s rises to 37%.

Andris noted that behaviors have a role to play in risk assessment. For this project, they were unable to account for community behavior, such as adherence to social distancing or wearing a mask, an area of study that only a few researchers have tapped into lately. 

Lawrence-based researcher Folashade Agusto is an assistant professor in the ecology and evolutionary biology department at the University of Kansas. In May, her team was awarded a one-year $199,999 Rapid Response Research (RAPID) grant from the National Science Foundation. She’s the lead investigator. 

People are motivated to act in ways that align with their beliefs, she said. So, part of what determines whether folks decide to congregate or not is their perception of risk, trust in information they consume and other public behaviors such as mask-wearing or social distancing. In a nutshell, Agusto’s team is looking at the way in which people operate during a pandemic and why.

“We are just trying to understand how people’s behavior would impact the dynamics of the disease spread,” she said.

Whereas Georgia Tech’s research attempts to predict risk, the KU team’s research attempts to understand the motivators. 

Agusto said when it comes to human behavior, a couple of things drive that. People’s religious beliefs, cultural practices, and previous knowledge about a disease and information that they can glean from the disease in the news or social media. 

“So all these factors (influence) human behavior,” she said. 

So, those who look at Georgia Tech’s map and deem a 20% risk as low will likely meet up, unphased by a rise in cases in their county. 

This is part of what Agusto’s team is trying to analyze.

Though the KU team is still in the early stages of research, their first results show that breaking quarantine causes virus outbreaks.  The more folks quarantine, the less the disease spreads. This aligns with the Centers for Disease Control and Prevention’s guidance.

But that is contingent on people complying with good hygiene, mask-wearing and social distancing. To reduce the defiance behavior, for instance, the group theorized that governments should incentivize isolation. 

“One behavior will lead to this multiple wave of infection that we’ve been hearing. (Not just that), it also leads to the persistence of the disease in the community,” Agusto explained. 

The RAPID project at KU is a three-step model. The first step incorporates human behavior. The second will incorporate demographics such as the number of adults, children and seniors. The third will use the previous two models’ information to analyze “regional-control efforts,” statewide and nationwide.

It could help inform policymakers and disease-control agencies on what next steps are best to contain the spread and what measures to implement moving forward. 

A. Townsend Peterson, a disease ecologist at the University of Kansas, said policymakers should take two things into account when trying to understand the virus.

“Consistency of message,” Peterson said. “Imagine that the federal government could speak with one voice instead of the governor saying one thing and the legislature saying another. 

All that amplifies into perception of risk and doubt and lack of confidence in the system.”

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