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

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 (polymerase chain reaction) 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 of temporal associations in which some pairs, such as SARS-CoV-2 and human seasonal coronaviruses, exhibit positive associations in their temporal incidence patterns, primarily from SARS-CoV-2 to human seasonal coronaviruses, whereas others, such as influenza and rhinovirus or parainfluenza virus show negative associations in circulation dynamics. Associations were often directional rather than reciprocal: for instance, rhinovirus was negatively associated with subsequent human seasonal coronaviruses, whereas the reverse pattern was not observed, while positive bidirectional associations between human metapneumovirus and parainfluenza virus were observed in several models. Temporal association patterns were largely consistent across analytical frameworks, suggesting persistent co-circulation dynamics among the studied respiratory viruses. By integrating multiple analytic frameworks, our study provides a comprehensive, data-driven view of patterns of co-circulation and statistical association among respiratory viruses, offering crucial insights for improved epidemic forecasting and mitigation strategies.