Computer Model Predicts Public Response to Disease Outbreaks

Researchers at MIT (Cambridge, MA, USA), the Draper Laboratory (Cambridge, MA, USA), and Ascel Bio (Larchmont, NY, USA), developed a coupled network approach to understanding and predicting social response to disease spread and the panic spread processes, modeling them through local interactions between agents. The model found that the social contagion process depends on the prevalence of the disease, its perceived risk, and a global media signal.

To verify the model, the researchers analyzed the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City, and the 2003 severe acute respiratory syndrome (SARS) outbreak and 2009 H1N1 outbreaks in Hong Kong. The model accurately predicted that public response was disproportionate to actual risk; in general, the research showed, diseases that are rare or unusual frequently receive attention that far outpaces the true risk. For example, the SARS outbreak produced a much stronger public response than H1N1, even though the rate of infection with H1N1 was hundreds of times greater than that of SARS. The study was published on January 15, 2015, in Interface.

“Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed,” concluded lead author Natasha Markuzon, PhD, of the Draper Laboratory. “When confronted with fatal or novel pathogens, people exhibit a variety of behaviors from anxiety to hoarding of medical supplies, overwhelming medical infrastructure, and rioting.”

“I hope in the future, if we could predict that these bad social and economic consequences are going to happen that might cost a lot of money and might cost a lot of lives, that people can take measures to counteract these effects,” added study coauthor Marta Gonzalez, PhD, an assistant professor of civil and environmental engineering at MIT. “While media coverage can sometimes help to spread panic during an outbreak, the right kind of information can potentially have the opposite effect.”

Related Links:
MIT
Draper Laboratory
Ascel Bio