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

Abstract

Kinsa Inc. sells Food and Drug Administration–cleared smart thermometers, which synchronize with a mobile application, and may aid influenza forecasting efforts. We compare smart thermometer and mobile application data to regional influenza and influenza-like illness surveillance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Smart thermometer readings are highly correlated with regional surveillance data, are more predictive of surveillance data for their own region and season than for other times and places, and improve predictions of influenza, but not predictions of influenza-like illness. These results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.

RETRACTED ARTICLE: Thermodynamic imaging calculation model on COVID-19 transmission and epidemic cities risk level assessment—data from Hubei in China

“…Different from the rough set theory, the thermodynamic image principle has higher visualization value and imaging value. A smart thermometer and mobile application was proposed for influenza prediction, but we mainly proposed a thermodynamic imaging to monitor and predict epidemiological trends, resulting in a more intuitive image [ 16 ]. An infectious disease analysis framework was proposed for analyzing the dynamics of infectious diseases and proposing interventions [ 17 ].…”

Prevalence of Asymptomatic SARS-CoV-2 Infection

“…A case series from Wuhan, China, from 24 December 2019 to 24 February 2020 included data for "78 patients from 26 cluster cases of exposure to the Hunan seafood market or close contact with other patients with COVID-19" (30). Asymptomatic patients "were younger (median [interquartile range] age, 37 [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] years vs 56 years; P < .001), and had a higher proportion of women (22 [66.7%] women vs 14 [31.%] [sic] women; P = .002). "…”

“…Until that is possible, innovative surveillance tactics might provide useful data for public health officials. Self-monitoring with internet-connected thermometers and smart watches that monitor heart rate, then crowdsourcing the resulting data, has been shown to accurately predict the incidence of influenza-like illness as reported by the California Department of Public Health and the Centers for Disease Control and Preven-tion (35)(36)(37). Similarly, monitoring sewage sludge provided "SARS-CoV-2 RNA concentrations [that] were a seven-day leading indicator ahead of compiled COVID-19 testing data and led local hospital admissions data by three days" (38).…”

The hopes and hazards of using personal health technologies in the diagnosis and prognosis of infections

“…32 The Robert Koch institute, Berlin, Germany, has launched a similar fever trend tracker based on resting heart rate and activity data from more than 500 000 participants. 31 Kinsa smart thermometers (San Francisco, CA, USA) have also shown utility in predicting influenza-like illness activity 29,33,34 and potentially COVID-19. 30 Novel data streams from sensors can offer key insight into trends, timing of outbreaks, and identifying specific geo graphical hotspots of infec tion.…”