Smartphone-based sensors may enable real-time surveillance of infectious diseases at population and household levels. This study evaluates the use of data from commercially available 'smart thermometers,' connected to a mobile phone application, for the surveillance of influenza-like illness (ILI).
At a population level, we analyze the correlation between thermometer recordings (total readings and fevers) and CDC-reported ILI activity nationally and by age group and region. We develop time-series models to forecast ILI activity in real-time and up to three weeks in advance. At an individual level, we analyze the ability of thermometer readings to track the duration of fevers and identify biphasic fever patterns. We also investigate potential transmission of febrile illness within households among device users.
Thermometer readings are highly correlated with national ILI activity (r>0.95) and activity patterns across regions and age groups. Thermometer readings also significantly improve forecasts of ILI activity in real-time and up to three weeks in advance. In-household results find that fevers lasting between 3-6 days and bi-phasic fever episodes occur more frequently during the influenza season. In addition, potential cases of in-household transmission of febrile illness originate more frequently from children than adults.
Smart thermometers represent a novel source of information for influenza surveillance and forecasting. Not only do thermometer readings capture real-time ILI activity at a population level, they can also be used to generate improved forecasts. Moreover, the widespread deployment of these smart thermometers may also allow for more rapid and efficient surveillance at the household level.
This research output has an Altmetric Attention Score of 208. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 December 2022.
All research outputs
#232,733
of 33,503,100 outputs
Outputs from Clinical Infectious Diseases
#472
of 20,524 outputs
Outputs of similar age
#4,429
of 487,036 outputs
Outputs of similar age from Clinical Infectious Diseases
#6
of 198 outputs
Altmetric has tracked 33,503,100 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 20,524 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.0. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 487,036 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 198 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.
