This hybrid surveillance validation study assessed the temporal alignment between metagenomic wastewater-based epidemiology (WBE) signals and clinical indicators of respiratory illness. The analysis focused on sequencing-derived viral abundance for SARS-CoV-2, influenza (A and B), and respiratory syncytial virus (RSV) and compared those wastewater signals with two clinical datasets to evaluate correlation strength, lead/lag timing, and geographic consistency.
The study used data collected through the Texas Wastewater Environmental Biomonitoring (TexWEB) program across 10 counties: Cameron, Chambers, El Paso, Fort Bend, Harris, Hays, Lubbock, Travis, Wichita, and Williamson. Sampling spanned June 2022–June 2024. Four counties (El Paso, Fort Bend, Harris, and Lubbock) had continuous coverage across the full 24-month study period; the other counties had available data for June 2023–June 2024. The jurisdictions sampled include diverse demographic and urban profiles, with major metropolitan representation such as Houston (Harris County).
Wastewater samples were obtained from the TexWEB network, which monitors 41 publicly owned treatment works across Texas. This analysis included 23 sites that maintained near-weekly sampling and consistent sequencing-based reporting for the targeted respiratory viruses during June 2022–June 2024. Viral abundance was quantified using a metagenomic metric, reads per million filtered (RPMF), which reflects a genomewide, variant-inclusive relative signal rather than an absolute concentration. For counties with multiple sampling sites, researchers computed a population-weighted county-level RPMF to facilitate comparisons with clinical datasets that are aggregated at broader population levels.
The metagenomic sequencing approach supports simultaneous detection and variant characterization across many viruses and is less susceptible to primer-mismatch errors that can affect PCR-based assays. However, RPMF represents relative abundance and does not produce gene-copy-per-volume values typical of some qPCR-based wastewater analyses.
Two complementary clinical data sources were used for validation: the National Syndromic Surveillance Program (NSSP) and the Texas All-Payer Claims Database (TX-APCD).
NSSP provides near–real-time emergency department (ED) visit indicators by integrating chief complaints and discharge diagnoses, including ICD-9/ICD-10-CM clinical coding. NSSP coverage includes a large proportion of Texas healthcare facilities and captures rapid shifts in community respiratory activity; in the Houston region, NSSP captures approximately 90% of facilities.
The TX-APCD offers administrative insurance claims that encompass a broader range of healthcare encounters beyond ED visits, giving a higher-resolution view of disease-associated healthcare utilization across demographics.
The study derived pathogen-specific clinical counts for COVID-19, influenza, and RSV from NSSP queries and matched those temporal series against county-level wastewater RPMF signals. Where relevant, authors discuss differences in clinical data capture that can affect alignment with wastewater-derived measures.
Across the evaluated counties and seasons, metagenomic wastewater signals for the three respiratory viruses produced moderate-to-strong temporal correlations with the clinical indicators examined. Influenza demonstrated the most stable associations with clinical data (reported correlations up to r <0.98 in the source), indicating consistent temporal alignment between wastewater RPMF and clinical metrics.
Signals for SARS-CoV-2 frequently preceded changes in clinical indicators, suggesting potential early-warning value of wastewater surveillance for that pathogen in the postpandemic context. By contrast, RSV exhibited higher geographic and temporal heterogeneity in wastewater–clinical alignment; the authors attribute this heterogeneity in part to variation in clinical data capture across jurisdictions and differences in how RSV cases present to and are recorded by healthcare systems.
The metagenomic WBE approach consistently tracked community-level trends despite challenges. Key operational constraints included sampling frequency, partial geographic alignment of wastewater catchments versus clinical reporting areas, and variable duration of site participation across counties (some with full 24-month coverage, others with 12 months).
The findings support metagenomic WBE as a resilient, complementary surveillance stream to conventional clinical data. The ability to detect and quantify multiple respiratory viruses simultaneously and to capture variant-inclusive signals makes metagenomic sequencing a useful adjunct when clinical testing declines or when clinical surveillance lacks completeness.
Influenza’s stable alignment with clinical indicators suggests that wastewater RPMF can reliably reflect community influenza activity across sites and seasons. The observation that SARS-CoV-2 wastewater signals tended to lead clinical metrics indicates potential for wastewater to provide situational awareness ahead of increases in clinical encounters, which is valuable in settings with reduced clinical testing.
Heterogeneity observed for RSV underscores that wastewater–clinical concordance may vary by pathogen and by locality, influenced by care-seeking behavior, differential capture in ED syndromic surveillance versus insurance claims, and the match between wastewater catchment boundaries and health data geographies. The metagenomic RPMF metric trades absolute quantification for breadth and sequencing resilience; practitioners should interpret RPMF as a relative indicator of community viral burden.
Operational limitations identified in this validation include sampling cadence, geographic misalignment between wastewater catchments and healthcare reporting units, and incomplete temporal coverage for some counties. These factors can dampen or complicate temporal correlations and should be addressed in program design when seeking precise lead/lag estimates.
Metagenomic WBE in Texas during June 2022–June 2024 tracked community trends for SARS-CoV-2, influenza, and RSV and showed moderate-to-strong temporal correlations with ED-based syndromic data (NSSP) and insurance claims (TX-APCD). Influenza demonstrated the most consistent wastewater–clinical associations, SARS-CoV-2 wastewater signals generally preceded clinical indicators, and RSV patterns were more variable across space and time. Despite sampling frequency and geographic alignment challenges, metagenomic wastewater surveillance provided robust situational awareness and can serve as a complementary tool to strengthen public health preparedness and monitoring in a postpandemic respiratory disease landscape.