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Smart thermometer to improve flu forecasting
March 18, 2018, 11:47 am
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A new approach tested by researchers at the University of Iowa shows that de-identified data from a ‘smart thermometer’ connected to a mobile phone app can track flu activity in real time at both population and individual levels and the data can be used to significantly improve flu forecasting.

Researchers found that 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.

Using simple forecasting models, the research team 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.

Analyzing de-identified data from commercially available ‘smart thermometers’ and their accompanying mobile app, the researchers recorded users' temperature measurement over a 28-month study period, from September 2015 to December 2017 and included over 8 million temperature readings generated by nearly 450,000 unique devices. The smart thermometers encrypted device identities to protect user privacy and also gave users the option of providing information on age or sex. Readings were reported from all 50 states in the United States and were aggregated to provide region and age-group specific flu activity estimates.

Researchers then 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 new 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.

Information of influenza activity received in advance can help alert health care professionals, coordinate response efforts, and anticipate clinic and hospital staffing needs and increases in visits associated with high levels of influenza activity. Knowing that flu activity is about to increase in a community may also prompt individuals to get a flu shot, stay home from work when they get sick, and seek medical help if their illness worsens.

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