Outbreaks of infectious diseases are occurring more frequently around the world. In the Democratic Republic of the Congo, the world’s second-largest Ebola outbreak has been going on for the past 10 months with no end in sight.
Dr. Ceyhun Eksin, lead author and assistant professor in the Department of Industrial and Systems Engineering at Texas A&M University, and his colleagues at the University of California, Santa Barbara and the Georgia Institute of Technology have published an article in the journal Epidemics that focuses on incorporating behavior change criteria into disease outbreak models. Adding these criteria will allow professionals and communities to mobilize adequate resources during epidemic outbreaks and reduce public mistrust caused by the overallocation of resources.
The research team hypothesized that individuals would take action during an outbreak to reduce their exposure by avoiding infected individuals. This behavior would change the number of individuals infected during the outbreak. Researchers use models to estimate the number of individuals who are at risk for being infected, are likely to become infected and of those infected, how many will recover during an outbreak. These estimates help communities mobilize resources during an outbreak.
The models used to predict the impact of an outbreak, called simple susceptible-infected-recovered (SIR) models, do not take the changes in an individual’s behavior into account and can overpredict the number of infected individuals during an outbreak. This can lead to an overuse of resources. Adding this knowledge to prediction models can help researchers more accurately predict the resources needed during an outbreak.
The research team created a modified SIR model that included the ability to pick up change in an individual’s behavior. By testing the modified model against the simple SIR model, Eksin and his colleagues were able to show that the modified model more accurately predicted outbreak numbers. By inputting past outbreak data into the modified models, they were able to predict the number of infected individuals more accurately.
“Our goal was to adapt these findings to forecast the disease trajectory, even if the initial information the model received was inaccurate. The findings show there is value to incorporating a behavior aspect into forecast models,” Eksin said.
Predicting the number of individuals who will become infected during an outbreak is valuable to determine how to use limited resources, and interdisciplinary research can help understand the link between a public health response and behavior change. If a community is better able to plan for an outbreak, without over-preparing, they can save resources and reduce the possibility of losing public support during future outbreaks.