BACKGROUND: Understanding virus-virus interactions is important for evaluating disease transmission and severity. Positive interactions suggest concurrent circulation, while negative interactions indicate reduced transmission of one virus when another is prevalent. This study examines interactions among seven respiratory viruses using a Bayesian approach that accounts for seasonality and long-term trends. METHODS: We analyzed data from 43,385 acute febrile illness cases in the Sentinel Enhanced Dengue Surveillance System in Puerto Rico (2013-2023). Viruses studied included influenza A (IAV), influenza B (IBV), respiratory syncytial virus (RSV), human parainfluenza viruses 1 and 3 (HPIV-1, HPIV-3), human adenovirus (HAdV), and human metapneumovirus (HMPV). Wavelet coherence analysis investigated synchronous or asynchronous viral co-variation, while a Bayesian hierarchical model estimated pairwise interactions. RESULTS: Among 43,385 participants, 26.0% tested positive for at least one virus, with IAV (9.5%), HAdV (4.1%), RSV (3.6%), and IBV (3.6%) being most frequent. Coinfections occurred in 0.5% of cases, often involving HAdV. Wavelet coherence identified significant synchronization among RSV/HMPV, HPIV-1/HMPV, and other virus pairs, with minimal coherence during the COVID-19 pandemic. Bayesian modeling suggested five virus-virus associations: four positive (RSV/HPIV-3, HMPV/HPIV-1, IBV/HAdV, IBV/HMPV) and one negative (IAV/HAdV). However, when restricting the analysis to the pre-pandemic period, fewer associations remained statistically credible. CONCLUSIONS: Respiratory viruses in Puerto Rico demonstrate patterns of co-circulation that may reflect complex interactions, but these associations appear context-dependent. Findings highlight the need for continued surveillance to better understand virus-virus dynamics and their implications for public health interventions.