Acoustic surveillance for respiratory diseases: a prospective analysis of cough trends using artificial intelligence.

Abstract

Syndromic surveillance for respiratory disease is limited by an inability to monitor its protean manifestation, cough. Advances in artificial intelligence provide the ability to passively monitor cough at individual and community levels. We hypothesized that changes in the aggregate number of coughs recorded among a sample could serve as a lead indicator for population incidence of respiratory diseases, particularly that of COVID-19. We enrolled over 900 people from the city of Pamplona (Spain) between 2020 and 2021 and used artificial intelligence cough detection software to monitor their cough. We collected nine person-years of cough aggregated data. Coughs per hour surged around the time cohort subjects sought medical care. There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population. We propose that a clearer correlation with COVID-19 incidence could be achieved with better penetration and compliance with cough monitoring.

Longitudinal passive cough monitoring and its implications for detecting changes in clinical status

“…As reported elsewhere [ 13 ], we collected over 9 person-years of cough data and 62 325 coughs between November 2020 and August 2021 from 616 participants who recorded at least 1 h of data, and 22.4% of whom reported a history of acute or chronic cough. In total, 178 participants registered >100 h of monitoring, and 21 registered at least 240 h ( figure 1 ).…”

“…This is an empiric statistical analysis of a larger cohort acoustic surveillance study. Sample size calculations and the study's main results have been described elsewhere [ 13 , 14 ].…”

“…This includes smartphone-based applications that employ acoustic AI to detect and track cough. We recently reported the use of one such tool to monitor cough among the inhabitants of Pamplona, Spain [ 13 ]. We now present a statistical analysis of the data collected in that cohort and explore its implications for patient care and drug development.…”