Scalable Genomic Integration into Syndromic Surveillance for Respiratory Outbreak Preparedness in Brazi

Timely detection of respiratory outbreaks is a key step for epidemic preparedness. Brazil’s Alert-Early System of Outbreaks with Pandemic Potential (ÆSOP) uses anomaly detections in the curves of Primary Health Care encounters data for syndromically detecting increases of influenza-like illnesses. Herein, we present a proof-of-concept study evaluating the integration of a cost-efficient genomic module into ÆSOP, using pooled-sample Next-Generation Sequencing with Hybrid Capture (NGS-HC) to characterize pathogens during both alert and baseline periods across seven cities, representing all Brazilian regions (n=1,137 samples; 114 pools). We performed RT-qPCR for major respiratory viruses in all samples, as a benchmark for NGS-HC results. NGS-HC detected 33 viral species, including Flu A, SARS-CoV-2, RSV, HRV, seasonal HCoVs, together with exploratory profiling of respiratory tract-associated bacteriaas well as respiratory tract-associated bacteria. Overall concordance with RT-qPCR was moderate (sensitivity 80.81%, specificity 81.27%, weighted kappa = 0.53). Pooling samples led to a tenfold cost-per-sample reduction, with a median turnaround time of 7 days. Syndromic alerts consistently coincided with high impact in public health pathogens, while in non-alert periods, there was a predominance of endemic, lower-impact viruses. There was a marked regional and seasonal variation in genomic profiles, indicating the need for a national baseline pathogen landscape. We addressed amplicon contamination challenges through laboratory protocols and stringent bioinformatic pipelines. Our findings support the integration of genomic surveillance for early outbreak detection in decentralized health systems, providing a scalable model for low- and middle-income countries to enhance epidemic preparedness.