Population-level associations in the spread of co-circulating respiratory viruses: A multi-method statistical investigation using incidence data

Abstract Respiratory infections remain a major global health burden, causing substantial morbidity and mortality worldwide. The responsible viruses circulate concurrently, potentially affecting each other’s dynamics, yet the extent and direction of such interactions remain poorly understood. Characterising these cross-pathogen effects at the population level is essential for elucidating transmission dynamics and guiding mitigation strategies. Using incidence data from a participatory syndromic surveillance system with multiplex PCR confirmation of specific pathogens, we applied complementary statistical approaches, including multivariate regression, endemic–epidemic, and distributed-lag models, to characterise immediate and delayed associations among seven major respiratory diseases. We show that these pathogens form a connected system in which some, such as SARS-CoV-2 and human seasonal coronaviruses, enhance each other’s transmission, whereas others, notably influenza, inhibit the concurrent circulation of competitors such as rhinovirus or parainfluenza virus. Effects were often directional rather than reciprocal: for instance, rhinovirus inhibited human seasonal coronaviruses but not vice versa, while mutual enhancement between human metapneumovirus and parainfluenza virus appeared across several models. Interaction patterns were time-dependent yet largely consistent, indicating persistent ecological interference among co-circulating respiratory viruses. By integrating multiple analytic frameworks, our study provides a comprehensive, data-driven view of how respiratory viruses coexist and compete, offering crucial insights for improved epidemic forecasting and mitigation strategies.