Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain. Real-time tracking of the COVID-19 pandemic and detection of the emergence of novel variants or other respiratory pathogens represent challenges for public health authorities globally..
This paper proposes an observational protocol to monitor and analyze acoustic data (like cough sounds) to identify potential outbreaks rapidly. It aims to develop a digital acoustic surveillance system for the early detection of respiratory disease outbreaks in Spain.
The researchers are utilizing digital devices, such as smartphones and environmental sensors, running AI powered cough detection technology, to record acoustic oustic data in selected regions of Spain. Advanced algorithms and artificial intelligence techniques are applied to the collected acoustic data to detect patterns indicative of respiratory diseases.
The study will be conducted in diverse regions across Spain, possibly targeting areas with high population densities or previous records of significant respiratory disease outbreaks.
Implications for Public Health Policy: Successful implementation of this protocol could influence public health policies, especially in adopting more digital and AI-based approaches for disease surveillance and response.