'Smart thermometer' could predict flu spread
The smart thermometers encrypt device identities to protect user privacy and also give users the option of providing anonymised information on age or sex
A "smart thermometer" connected to a mobile phone app can track flu activity in real time and help predict how the infection will spread, scientists say.
"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," said Aaron Miller, a postdoctoral scholar at University of Iowa (UI) in the US.
"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," said Miller.
"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," he said.
Scientists analysed de-identified data from a commercially available thermometer and accompanying app, which recorded users' temperature measurement over a study period from 2015 to 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 anonymised information on age or sex.
The team compared the data from the smart thermometers to influenza-like illness (ILI) activity data gathered by the US Centers for Disease Control and Prevention (CDC) from health care providers across the country.
The study, published in the journal Clinical Infectious Diseases, 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 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.
"Our findings suggest that data from smart thermometers are a new source of information for accurately tracking influenza in advance of standard approaches," said Philip Polgreen, associate professor at UI.
"More advanced information regarding influenza activity can help alert health care professionals that influenza is circulating, help coordinate response efforts, and help anticipate clinic and hospital staffing needs and increases in visits associated with high levels of influenza activity," he said.
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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