Accounting for methodological variabilities in disease outbreak surveillance using wastewater-based epidemiology: a case study with SARS-CoV-2.

Abstract

Wastewater-based surveillance of SARS-CoV-2 has become an established approach for monitoring health status and disease outbreak at the community level. Although analytical protocols for RNA quantification have been widely implemented and optimised, the characterisation of variability introduced across all stages, such as storage, sample processing, and PCR quantification, remained limited, including its impact upon epidemiological interpretation. This study proposes an analytical framework to quantify variability across the complete workflow for SARS-CoV-2 RNA measurement in wastewater, including storage stability, sample processing, and PCR quantification. This framework was developed for a large SARS-CoV-2 dataset of two viral gene targets (N1, E-Sarbeco), which were quantified alongside human-associated CrAssphage for a total of 20,124 RT-qPCR data points derived from four water recycling centres (WRCs) over a 24-month period. This analysis revealed that sample refrigeration yielded richer epidemiological data than freezing, and that CrAssphage had minimal utility as a normalisation biomarker given its higher variability. Quantification of analytical variability for SARS-CoV-2 targets enabled development of a composite metric (Total Estimated SARS-CoV-2), improving sensitivity and reducing non-detects at the expense of reduced precision. This novel multi-stage framework had the capability to improve the sensitivity and interpretation of WBE data and the characterisation of WBE methodologies, with broad applicability for pathogen monitoring.