Analyzing trends on Twitter and Google can help predict vaccine scares that can lead to disease outbreaks, according to a study from the University of Waterloo.
In the study, researchers examined Google searches and geocoded tweets with the help of artificial intelligence and a mathematical model. The resulting data enabled them to analyze public perceptions on the value of getting vaccinated and determine when a population was getting close to a tipping point.
In the study, a tipping point represented the point at which vaccine coverage declines dramatically due to spreading fear, which could cause large disease outbreaks due to a loss of population immunity.
"What this study tells us is that the same mathematical theories used to predict tipping points in phenomena such as changing climate patterns can also be used to help predict tipping points in public health," said Chris Bauch, a professor of applied mathematics at Waterloo. "By monitoring people's attitudes towards vaccinations on social media, public health organizations may have the opportunity to direct their resources to areas most likely to experience a population-wide vaccine scare, and prevent it before it starts."