Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks
Condition: Covid19; Cough
MeSH terms: COVID-19; Respiratory Tract Diseases; Respiration Disorders
Study type: Observational

Summary: 
      An observational study to evaluate the accuracy of a digital cough monitoring tool to reflect
      the incidence of COVID-19 and other respiratory infections at the community level in the city
      of Pamplona, Spain.
    

Detailed description: 
      This is a single-center prospective observational study that pretends to evaluate the
      accuracy of an acoustic surveillance mobile app to detect individual episodes of cough among
      a monitored population, as well as the barriers and facilitators that might affect uptake of
      similar platforms at a population level.

      The app in question, Hyfe cough tracker, runs in the background of smartphones, and records
      short snippets (<0.5 seconds) of explosive, putative cough sounds. These are then classified
      as cough or non-cough, using a convolutional neural network (CNN) model, and matched to GPS
      and time data collected by the smartphone.

      The night-time cough of participants will be monitored for a 30-day period, and their
      clinical records will be reviewed regularly, specifically looking for diagnoses of
      cough-producing diseases, and with special emphasis on COVID-19.

      Cough data will be used to create a heatmap of cough density and geographic distribution.
      Aggregated cough registries will be used to calculate the coughs per person-hour registered
      in the cohort. These data will be used to carry out an ARIMA analysis on three parallel time
      series at the community level: The incidence of respiratory disease in the monitored cohort,
      in the entire study area (including the Universidad de Navarra, and the neighbouring Cendea
      de Cizur), and the cough frequency per monitored hours.

      Changes in cough frequency will also be compared to other environmental variables such as
      temperature and pollution level registered in the study area.
    

Intervention name(s): Hyfe cough tracker
Intervention type(s): Device
Intervention description(s): A mobile app that runs in the background of smartphones and detects putative cough sounds.
Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Participants will be recruited in the Cendea de Cizur, a municipality composed by a cluster
        of villages south of the city of Pamplona, and the neighbouring town of Cizur Mayor, in the
        Comunidad Foral de Navarra (Spain), as well as in the different campuses of the University
        of Navarra. The 4,000 people living in the Cendea de Cizur are served by a public health
        center which receives 45,000 outpatients visits per year. Of these, approximately 12% are
        associated with respiratory diseases. The University of Navarra has over 11,000 registered
        students, 900 professors and over 600 other employees. Both the Cendea de Cizur and the
        University are served by the Clínica Universidad de Navarra, the largest private health
        center in the region, which provides medical care to an estimated population of over
        100,000 people.
      
Keywords: chronic cough; Covid19; Acoustic surveillance