Real-time data from smartphone thermometers can effectively track and predict influenza activity at national and regional levels.

“We found the smart thermometer data are highly correlated with information obtained from traditional public health surveillance systems and can be used to improve forecasting of influenza-like illness activity, possibly giving warnings of changes in disease activity weeks in advance,” says lead study author Aaron Miller, PhD, a UI postdoctoral scholar in computer science.

“Using simple forecasting models, we showed that thermometer data could be effectively used to predict influenza levels up to two to three weeks into the future,” Miller says. “Given that traditional surveillance systems provide data with a lag time of one to two weeks, this means that estimates of future flu activity may actually be improved up to four or five weeks earlier.”

The findings were published online Feb. 8 in the journal Clinical Infectious Diseases

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