An Autonomous Pathogen Detection System for Assessing Temporal Associations with Respiratory Pathogen Activity

Current respiratory pathogen surveillance is largely based on case reporting, sentinel surveillance, and environmental sampling. This study developed an autonomous pathogen detection system (APDS) integrating real-time bioaerosol sensing, intelligent triggering, aerosol collection, nucleic acid extraction, real-time PCR, and automated data reporting for respiratory pathogen surveillance. System performance was evaluated in a 20 m³ aerosol chamber and through 52 weeks of continuous monitoring in a hospital outpatient hall in Beijing. Laboratory experiments showed that the developed platform enables the detection of airborne influenza A virus at concentrations as low as 3.75 × 10⁴ 50% tissue culture infectious dose/m³ and can detect airborne Pseudomonas aeruginosa at concentrations as low as 1.80 × 10⁴ copies/m³. The platform can detect low concentrations of airborne pathogen nucleic acids that are undetectable by surface sampling. During field monitoring, APDS autonomously triggered 381 tests and recorded 46 pathogen-positive detections. Respiratory syncytial virus showed significant correlations when APDS signals preceded national surveillance data by 1-3 weeks, with the strongest correlation at a 3-week lead (ρ = 0.487). Given the spatial mismatch between single-site APDS monitoring and national surveillance data, this association should be considered exploratory. These findings demonstrate the feasibility of APDS for long-term automated aerosol surveillance and suggest its potential as a complementary tool for respiratory pathogen monitoring.