Characterization of temporal and spatial patterns of clinical respiratory episodes in growing pigs using continuous sound monitoring and an algorithm-based respiratory distress index
The purpose of this analysis was to characterize the temporal and spatial patterns of clinical episodes of swine respiratory diseases of growing pigs under large-scale commercial production conditions using a continuous audio monitoring system.
Materials and methods
Audio monitoring devices (SOMO+ Respiratory Distress Monitor, SoundTalks NV, Leuven, Belgium) were obtained and installed in a large 25 meter wide by 75 meter long commercial single airspace wean-to-finish facility designed to house 2400 pigs from weaning to market. In total, 11 devices were installed representing 11 zones in the airspace, with four devices over the middle of the pens on each side of the building spaced equidistant from each other and three in the central alleyway spaced equidistant from each other.
Pigs were placed into the facility per normal practice. An algorithm-based respiratory distress index (RDI) was continuously generated from recorded sound files and uploaded to a cloud-based database. RDI’s were continuously monitored and alerts were automatically sent to pre-determined personnel when a significant rise in RDI was detected by the system. When an RDI alert was generated, diagnostic samples were collected and tested by PCR for PRRS, IAV-S, Mycoplasma hyopneumoniae, PCV2 and parainfluenza.
RDI episodes were detected in the animal cohort at this site, including: IAV-S (H1N1), IAV-S (H3N2), and Mycoplasma hyopneumoniae. Distinct differences in the temporal RDI patterns were observed between IAV-S and Mycoplasma hyopneumoniae. Spatial patterns indicated RDI episode onset occurred in specific zones and subsequently spread throughout the airspace.
An audio monitoring system can be used to detect the onset and location of clinical respiratory events in commercial growing pig cohorts, enabling more targeted and timely interventions.
Key words: temporal, spatial, sound, respiratory, monitoring