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

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.