UT-Arlington Researcher Uses Agent-based Simulation to Explore Flu Transmission Within Public Indoor Spaces
It doesn’t take a researcher to know that going to a crowded, indoor location such as a shopping mall during flu season puts you at higher risk for exposure to the flu. It does take a researcher, however, to really understand how the process of disease transmission in a specific type of space work, and to use that information to generate ways to reduce transmission.
A recent study led by Dr. Yuan Zhou, assistant professor of engineering at The University of Texas at Arlington, uses a computer modeling approach called agent-based simulation to do just that. Zhou and her colleagues’ findings reveal, among other things, that mall employees are typically the “transmission hubs” in the process, that infection rates are likely to peak on the weekends and during holiday season, that Black Friday is absolutely the worst day to go to the mall if you want to avoid getting the flu, and that making sure that as many employees as possible are vaccinated could make a huge difference in the transmission rates.
Malls, Zhou said, are a relatively understudied space with respect to flu and other infectious diseases. Most disease transmission research, she said, focuses on larger spatial regions like countries and cities, rather than smaller public arenas like a shopping mall. To remedy this, she and her colleagues began by spending several months observing and analyzing individual shopper behavior in malls to create a profile of mobility and contact attributes to feed into their simulation. The resulting model looks at the dynamic behavior of both shoppers and employees to reveal how their interactions can increase transmission.
“Employees come into contact with more people, and so can be seen as ‘super-spreaders,’ but shoppers’ mobility is much more dynamic. They move a lot, and have different ways of making contact transmission while waiting in line, sitting together, interacting with employees, etc.,” Zhou said.
The goal of studies like these is to take the anecdotal and give it powerful and quantitative validity.
“We wanted to see if the model could give us evidence rather than intuition,” Zhou said.
Zhou and her team are now able to reliably say that the riskiest day for possible flu transmission is on Black Friday, when the mall is both crowded and open for much longer hours than is typical (as many as 22). The least risky days are non-holiday-season Sundays. They are also able to look at specific interventions and analyze which interventions, at which times and places, would be most effective.
The obvious solution to disease transmission on risky days like Black Friday, said Zhou, would be to just close the entire mall. That’s not, however, a practical socio-economic response. So, Zhou’s team ran the Black Friday simulation through a variety of mall-hour reductions to see if there was a sweet spot.
Reducing the time the mall is open by two hours resulted in a 19% decrease in the number of new cases of the flu likely to be transmitted. Cutting it down by four hours took it to 26% fewer, and eleven hours cut the rate in more than half. This doesn’t mean Zhou wants malls to close for half of Black Friday. Rather, she wants the results to be considered in a broader context. If mall managers, public health officials, and consumers understood the risks more thoroughly, they might adopt different policies or attitudes. She and her colleagues also found similarly positive results in their models when, instead of cutting down the number of hours the mall is open, they raised the rates of flu vaccination among employees.
With this baseline understanding of risk factors and contact transmission, Zhou said she hopes to develop a more sophisticated model for simpler intervention.
“Most people know that you should get a vaccine and wash your hands regularly. But if we know about individual risks at these locations, then we could possibly have more specific and effective policy-level interventions to control the disease at the macro level and then contribute to the larger scale of impact,” Zhou said. “We want to start from the bottom and then contribute to the top.”
The next step for Zhou and her team is to expand into other public spaces. She is currently working on a university-supported grant to apply the same framework to a college campus.