Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

Area of Science:

• Infectious disease epidemiology
• Public health surveillance
• Molecular diagnostics

Background:

• Accurate, real-time infectious disease monitoring is crucial for public health preparedness.
• Existing surveillance systems face challenges in scalability, specificity, and rapid reporting while maintaining patient privacy.

Purpose of the Study:

• To demonstrate automated, privacy-preserving export, aggregation, and analysis of infectious disease diagnostic test results.
• To assess the system's utility in monitoring seasonal respiratory pathogen occurrence and compare its efficiency to current surveillance methods.

Main Methods:

• Utilized BioFire Syndromic Trends, a syndrome-based yet pathogen-specific reporting system.
• Deidentified patient test results from the BioFire FilmArray system were aggregated into a cloud database.
• Analyzed over 362,000 patient samples from 20 clinical laboratories across the U.S. for pathogen prevalence, seasonality, and coinfections.

Main Results:

• Observed distinct seasonality for most pathogens; rhinovirus showed fall/spring peaks, while adenovirus and bacteria were detected year-round.
• Pathogen codetections occurred at an average rate of 7.94%, aligning with ecological predictions based on organism abundance.
• Demonstrated near real-time data display on the Syndromic Trends public website.

Conclusions:

• BioFire Syndromic Trends effectively preserves patient privacy while yielding valuable data on bacterial and viral pathogens.
• The system offers rapid reporting, with results uploaded within hours, significantly faster than traditional systems.
• Overcame administrative barriers related to privacy and data ownership, paving the way for high-resolution epidemiological analysis and outbreak detection.