One of Google’s public data-driven prediction systems has caught a cold, according to weighty new research…

“Google Flu Trends, which launched in 2008, monitors web searches across the US to find terms associated with flu activity such as “cough” or “fever”. It uses those searches to predict up to nine weeks in advance the number of flu-related doctors’ visits that are likely to be made. The system has consistently overestimated flu-related visits over the past three years, and was especially inaccurate around the peak of flu season — when such data is most useful.”

The doctors prescribe taking a healthy dose of national health statistics…

“Merely projecting current CDC data [doctors’ visits as recorded at the US Centers for Disease Control and Prevention] three weeks into the future yields more accurate results than those compiled by Google Flu Trends. Combining the two resulted in the most accurate model of all.”

Although one has to wonder about prediction feedback loops here. What if Google Flu Trends was actually right? But that Trends-watching doctors, carers and the public all put into effect various extra measures that stopped the Trends prediction from coming true in the longer-term six-to-nine week window? Or what about some kind of media amplification loop: more media chatter hits the news as the epidemic surfaces into the public mood, meaning that non-sufferers start using the relevant keywords more in social media?