Estimating viral interference from wastewater surveillance: a Bayesian multi-pathogen SEIR framework.

Authors

Affan Shoukat, Mahsa Moosavi, Nicole Hansmeier, Jianhong Wu

Published in

Mathematical biosciences. Pages 109845. Nov 02, 2026. Epub Nov 02, 2026.

Abstract

The concurrent circulation of influenza, SARS-CoV-2, and RSV creates complex transmission dynamics in which infection with one virus may reduce or enhance transmission of another. We developed a Bayesian multi-pathogen SEIR model in which the transmission of each virus is reduced by the prevalence of co-circulating pathogens, quantified by an aggregate population-level interference coefficient θ. We fit the model to weekly wastewater data from six Canadian provinces over the 2024-2025 respiratory season, avoiding the use of clinical data, which are limited by under-reporting and inconsistent testing. Our findings show the estimated interference corresponded to a 4 to 30% reduction in peak transmission of each virus during co-circulation, with smaller reductions in the effective reproduction number. Although these estimates are based on a single respiratory season and can vary under different patterns of co-circulation, they are consistent with recent studies on multi-pathogen viral interference using clinical surveillance data.

PMID:
42971423
Bibliographic data and abstract were imported from PubMed on 03 Nov 2026.

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