691. Real-time Local Influenza Forecasting using Smartphone-Connected Thermometer Readings.
Session: Poster Abstract Session: Public Health: Epidemiology and Outbreaks
Thursday, October 4, 2018
Room: S Poster Hall
  • ILI_IDWeek_poster.pdf (1.1 MB)
  • Background: Information regarding influenza activity can inform clinical and public health activities. However, current surveillance approaches induce a delay in influenza activity reports (typically 1-2 weeks). Recently, we used data from smartphone connected thermometers to accurately forecast real-time influenza activity at a national level. Because thermometer readings can be geo-located, we used state-level thermometer data to determine if these data can improve state-level surveillance estimates.

    Methods: We used temperature readings collected by the Kinsa smart-thermometer and mobile device app to develop state-level forecasting models to predict real-time influenza activity (1-2 weeks in advance of surveillance reports). We used state-reported influenza-like illness (ILI) to represent state influenza activity for 48 US states with sufficient surveillance data. Counts of temperature readings, fever episodes and reported symptoms were computed by week. We developed autoregressive time-series models and evaluated model performance in an adaptive out-of-sample manner. We compared baseline time-series models containing lagged state-reported ILI activity to models incorporating exogenous thermometer readings.

    Results: A total of 10,262,212 temperature readings were recorded from October 30, 2015 to March 29, 2018. In nearly all of the 48 states considered, weekly forecasts of ILI activity improved considerably when thermometer readings were incorporated. On average, state-level forecasting accuracy improved by 23.9% compared to baseline time-series models. In many states, such as PA, NM, MA, VA, NY and SC, out-of-sample forecast error was reduced by more than 50% when thermometer data was incorporated. In general, forecasts were most accurate in states with the greatest number of device readings. During the 2017-2018 influenza season, the average improvement in forecast accuracy was 24.4%, and thermometer readings improved forecasting accuracy in 41, out of 48, states.

    Conclusion: Data from smart thermometers accurately track real-time influenza activity at a state level. Local surveillance efforts may be improved by incorporating such information. Such data may also be useful for longer-term local forecasts.

    Aaron Miller, PhD1, Inder Singh, MBA, MS, MPP2, Sarah Pilewski, BS2, Vladimir Petrovic, PhD2 and Philip M. Polgreen, MD3, (1)Epidemiology, University of Iowa College of Public Health, Iowa City, IA, (2)Kinsa, Inc., San Francisco, CA, (3)Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA


    A. Miller, None

    I. Singh, Kinsa Inc.: Board Member , Employee and Shareholder , equity received and Salary .

    S. Pilewski, Kinsa Inc.: Employee and Shareholder , equity received and Salary .

    V. Petrovic, Kinsa Inc.: Employee and Shareholder , equity received and Salary .

    P. M. Polgreen, None

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