Aaron C Miller1, Inder Singh2, Erin Koehler2, Philip M Polgreen3. 1. Department of Computer Science, University of Iowa, Iowa City. 2. Kinsa, Inc, San Francisco, California. 3. Departments of Internal Medicine and Epidemiology, Carver College of Medicine, University of Iowa, Iowa City.
Abstract
Background: 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 surveillance of influenza-like illness (ILI). Methods: At a population level, we analyzed the correlation between thermometer recordings and Centers for Disease Control and Prevention-reported ILI activity nationally and by age group and region. We developed time-series models to forecast ILI activity in real time and up to 3 weeks in advance. We analyzed the ability of thermometer readings to track the duration of fevers and identify biphasic fever patterns. We also investigated potential transmission of febrile illness within households among device users. Results: Thermometer readings were highly correlated with national ILI activity (r > 0.95) and activity patterns across regions and age groups. Thermometer readings also significantly improved forecasts of ILI activity in real time and up to 3 weeks in advance. We found that fevers lasting between 3 and 6 days and biphasic fever episodes occurred more frequently during the influenza season. In addition, potential cases of in-household transmission of febrile illness originated more frequently from children than adults. Conclusions: Smart thermometers represent a novel source of information for influenza surveillance and forecasting. Thermometer readings capture real-time ILI activity at a population level, and 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.
Background: 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 surveillance of influenza-like illness (ILI). Methods: At a population level, we analyzed the correlation between thermometer recordings and Centers for Disease Control and Prevention-reported ILI activity nationally and by age group and region. We developed time-series models to forecast ILI activity in real time and up to 3 weeks in advance. We analyzed the ability of thermometer readings to track the duration of fevers and identify biphasic fever patterns. We also investigated potential transmission of febrile illness within households among device users. Results: Thermometer readings were highly correlated with national ILI activity (r > 0.95) and activity patterns across regions and age groups. Thermometer readings also significantly improved forecasts of ILI activity in real time and up to 3 weeks in advance. We found that fevers lasting between 3 and 6 days and biphasic fever episodes occurred more frequently during the influenza season. In addition, potential cases of in-household transmission of febrile illness originated more frequently from children than adults. Conclusions: Smart thermometers represent a novel source of information for influenza surveillance and forecasting. Thermometer readings capture real-time ILI activity at a population level, and 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.
Authors: Sina Akbarian; Mark P Nelder; Curtis B Russell; Tania Cawston; Laurent Moreno; Samir N Patel; Vanessa G Allen; Elham Dolatabadi Journal: IEEE J Transl Eng Health Med Date: 2021-12-30
Authors: Fred S Lu; Andre T Nguyen; Nicholas B Link; Mathieu Molina; Jessica T Davis; Matteo Chinazzi; Xinyue Xiong; Alessandro Vespignani; Marc Lipsitch; Mauricio Santillana Journal: PLoS Comput Biol Date: 2021-06-17 Impact factor: 4.475