KANSAS CITY, Mo. — Researchers from the University of Kansas are developing a series of mathematical models to evaluate disease-control measures aimed at stopping the spread of COVID-19.
The models will take into account human behavior, including public fear, perception of risk and adherence to or defiance of travel bans and quarantine orders.
"Most of the models that are out there for COVID-19 are not incorporating human behavior," said Folashade Agusto, an assistant professor of ecology and evolutionary biology at the University of Kansas. "That’s an important angle to incorporate into the models.”
Agusto and others on her team will produce three COVID-19 models that build onto one another.
The first model will incorporate public behavior and perceptions of risk and fear. The second will look at the demographics within a community, while the third model will use preceding behavior and demographic models to analyze regional-control efforts.
“We can essentially play with those settings and see what happens if infected people do not report themselves as having symptoms,” said Townsend Peterson, a disease ecologist and an evolutionary biology professor at the University of Kansas. “What’s the effect [of] that on the future accumulation of cases?
“Without having to wait for it to happen, we can essentially do little experiments and see what these behaviors mean as far as the future of the disease.”
The team earned a one-year, $199,999 Rapid Response Research award from the National Science Foundation.
“It doesn’t matter what that disease is,” said Jarron Saint Onge, an associate professor of sociology at the University of Kansas. “If you have an understanding of some of the characteristics, we can use these moving forward thinking about future epidemics or pandemics.”