Improving State-Level Influenza Surveillance by Incorporating Real-Time Smartphone-Connected Thermometer Readings Across Different Geographic Domains

Abstract

Background Timely estimates of influenza activity are important for clinical and public health practice. However, traditional surveillance sources may be associated with reporting delays. Smartphone-connected thermometers can capture real-time illness symptoms, and these geo-located readings may help improve state-level forecast accuracy. Methods Temperature recordings were collected from smart thermometers and an associated mobile phone application. Using temperature recordings, we developed forecasting models of real-time state-reported influenza-like illness (ILI) 2 weeks before the availability of published reports. We compared time-series models that incorporated thermometer readings at various levels of spatial aggregation and evaluated out-of-sample model performance in an adaptive manner comparing each model to baseline models without thermometer information. Results More than 12 million temperature readings were recorded from over 500,000 devices from August 30, 2015 to April 15, 2018. Readings were voluntarily reported from anonymous device users, with potentially multiple users for a single device. We developed forecasting models of real-time outpatient ILI for 46 states with sufficient state-reported ILI data. Forecast accuracy improved considerably when information from thermometers was incorporated. On average, thermometer readings reduced the squared error of state-level forecasting by 43% during influenza season and more than 50% in many states. In general, best-performing models tended to result from incorporating thermometer information at multiple levels of spatial aggregation. Conclusion Local forecasts of current influenza activity, measured by outpatient ILI, can be improved by incorporating real-time information from mobile-devices. Information aggregated across neighboring states, regions, and the nation can lead to more reliable forecasts, benefiting local surveillance efforts.

Assessing the utility of a smart thermometer and mobile application as a surveillance tool for influenza and influenza-like illness

“…Specifically, we note that at the multi-state level, these data were found to improve ILI predictions. 8,20 Future work may include using such data in influenza prediction contests, as well as using a hidden Markov model to compare crowdsourced and surveillance data to a virtual gold standard. The majority of previous participatory surveillance efforts have relied solely on self-reported fevers, in addition to self-reported symptoms.…”

Long-range local influenza forecasts via distributed syndromic monitoring: preliminary results

“…during an illness episode). The temperature readings are spatially aggregated and used to construct ILI time series that are highly correlated to Center for Disease Control and Prevention (CDC) ILI nationally (r > 0.95) and across CDC regions (r range 0.70-0.94) ( 7 , 8 ).…”

Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers

“…Networks of geolocated, user-generated physiological measurements hold the potential for improved tracking and prediction of outbreak epicenters ( 6, 7 ). Data from these networks are typically less specific, but more sensitive, than formal surveillance, and are often available more rapidly, because formal surveillance is constrained by testing speed ( 5 ) and/or time for record aggregation ( 8 ).…”

“…Readings are aggregated to county-scale ILI and anonymized. The temperature readings are used to construct an ILI signal that is highly correlated to Center for Disease Control and Prevention (CDC) ILI nationally ( r > 0.95) and across CDC regions ( r range 0.70-0.94), and these signals have been demonstrated to improve regional ILI surveillance and forecasting ( 6, 7 ). We construct the ILI signal at the county-scale, allowing identification of anomalous ILI incidence at the scale of individual cities.…”