Abstract
BACKGROUND: A viral infection can modify the risk to subsequent viral infections via cross-protective immunity, increased immunopathology, or disease-driven behavioral change. There is limited understanding of virus-virus interactions due to lack of long-term population-level data. METHODS: Our study leverages passive surveillance data of 10 human acute respiratory viruses from Beijing, Chongqing, Guangzhou, and Shanghai collected during 2009 to 2019: influenza A and B viruses; respiratory syncytial virus A and B; human parainfluenza virus (HPIV), adenovirus, metapneumovirus (HMPV), coronavirus, bocavirus (HBoV), and rhinovirus (HRV). We used a multivariate Bayesian hierarchical model to evaluate correlations in monthly prevalence of test-positive samples between virus pairs, adjusting for potential confounders. RESULTS: Of 101,643 lab-tested patients, 33,650 tested positive for any acute respiratory virus, and 4,113 were co-infected with multiple viruses. After adjusting for intrinsic seasonality, long-term trends and multiple comparisons, Bayesian multivariate modeling found positive correlations for HPIV/HRV in all cities and for HBoV/HRV and HBoV/HMPV in three cities. Models restricted to children further revealed statistically significant associations for another ten pairs in three of the four cities. In contrast, no consistent correlation across cities was found among adults. Most virus-virus interactions exhibited substantial spatial heterogeneity. CONCLUSIONS: There was strong evidence for interactions among common respiratory viruses in highly populated urban settings. Consistent positive interactions across multiple cities were observed in viruses known to typically infect children. Future intervention programs such as development of combination vaccines may consider spatially consistent virus-virus interactions for more effective control.