Interactions among acute respiratory viruses in Beijing, Chongqing, Guangzhou, and Shanghai, China, 2009-2019

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.

Keywords: acute respiratory infections; co‐infection; pathogen interactions; viral interference; viral synergism.

FIGURE 1

Laboratory‐detected respiratory viral infections for each month from January 2009 to December 2019 in four metro cities, China.

FIGURE 2

Frequency of confirmed co‐infections for each pair of respiratory viruses shown by size and proportion of each co‐infection pair among all tested samples shown by color from January 2009 to December 2019 in four metro cities, China.

FIGURE 3

Weighted Pearson's correlation coefficients for the total population studied and q‐values of monthly prevalence for each pair of respiratory viruses in four metro cities of China, where weights are the numbers of tests administered, China. The q‐values represent statistical evidence adjusted for multiple comparisons by controlling the false discovery rate. Significant correlations (q ≤ 0.10) are shown in color. Blue and red indicate positive and negative coefficients, respectively. Green and purple borders indicate virus pairs statistically significant with consistent directions in three and four cities, respectively.

FIGURE 4

Bayesian hierarchical model correlation coefficients for the total population studied and q‐values, adjusting for age, sex, seasonality, changes in testing frequency, and autocorrelation. The q‐values represent statistical evidence adjusted for multiple comparisons by controlling the false discovery rate. Significant correlations (q ≤ 0.10) are shown in color. Blue and red indicate positive and negative coefficients, respectively. Yellow, green, and purple borders indicate virus pairs statistically significant with consistent directions in two, three and four cities, respectively.