Year: 2026
Type: article
Abstract: ABSTRACT Wastewater-based epidemiology (WBE) has demonstrated its usefulness for population-level monitoring of many biomarkers in near-real time. As a source of data, it has continued potential across many applications, but with resources finite and uncertainties potentially large, methodological advancements are required for this potential to be achieved. Comprehensive data on COVID-19 provide an ideal case for experimentation with methodologies, providing insight and techniques that might generalise. To begin to address these challenges, we introduce and implement a modelling framework motivated by the Scottish COVID-19 wastewater programme. Using the generalised additive models for location, shape and scale methodology, we address multiple components of wastewater uncertainty observed in COVID-19 wastewater data that may be present elsewhere, including heterogeneous site uncertainty, laboratory batch effects and dilution effects where the dilution covariate is not completely reliable. Our estimates mitigate the impact of some of these factors and propagate uncertainty to generate plausible credible intervals. Building a simulation framework for evaluation, we find it is more effective in determining the overall trajectory of a disease than simpler methods, being able to detect a four-fold smaller increase in levels in one scenario. We conclude with an assessment of lessons learned for other WBE applications.
Source: Journal of Water and Health
DOI: 10.2166/wh.2026.169
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7225419994