Thursday, February 8, 2018

Big data from smart thermometer utilized to track and predict flu activity.

A study by researchers at the University of Iowa shows that anonymous data from a "smart thermometer" connected to a mobile phone app can track flu activity in real time at both population and individual levels. They also showed that this data can be used to improve flu forecasting. The study findings are published in the journal Clinical Infectious Diseases.

"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. 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."

Miller and senior study author Philip Polgreen, MD, UI associate professor of internal medicine and epidemiology, analyzed de-identified data from the commercially available Kinsa Smart Ear Thermometers and accompanying app, which recorded users' temperature measurement over a study period from Aug. 30, 2015 to Dec. 23, 2017. There were over 8 million temperature readings generated by almost 450,000 unique devices. The smart thermometers encrypt device identities to protect user privacy and also give users the option of providing anonymized information on age or sex. Readings were reported from all 50 states and were aggregated to provide region and age-group specific flu activity estimates.

The UI team compared the data from the smart thermometers to influenza-like illness (ILI) activity data gathered by the Centers for Disease Control and Prevention (CDC) from health care providers across the country. They found that the de-identified smart thermometer data was highly correlated with ILI activity at national and regional levels and for different age groups.

Current forecasts rely on this CDC data, but even at its fastest, the information is almost two weeks behind real-time flu activity. The UI study showed that adding thermometer data, which captures clinically relevant symptoms (temperature) likely even before a person goes to the doctor, to simple forecasting models, improved predictions of flu activity. This approach accurately predicted influenza activity at least three weeks in advance.

Miller notes that the smart thermometers also provide a way to estimate which age groups are being most affected during a flu season, using de-identified data. Monitoring the duration of fever from the smart thermometer readings also revealed that fevers occurring during flu season were more likely to last three to six days and much less likely to last only one day. Fevers lasting even or more days were not at all seasonal. The data also identified instances where users had fever that went away for a few days and then returned. The researchers believe this so-called "biphasic" fever pattern may reflect more serious illnesses. The second temperature spike can indicate a secondary bacterial infection like pneumonia that sets in after the flu and can lead to more severe health problems, especially in older individuals.

Citation:  Miller, Aaron C., Inder Singh, Erin Koehler, and Philip M. Polgreen. "A Smartphone-Driven Thermometer Application for Real-Time Population- and Individual-Level Influenza Surveillance." Clinical Infectious Diseases, 2018. doi:10.1093/cid/ciy073.

Adapted from press release by  University of Iowa.

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