Interactions among 17 respiratory pathogens: a cross-sectional study using clinical and community surveillance data

Abstract

Background Co-circulating respiratory pathogens can interfere with or promote each other, leading to important effects on disease epidemiology. Estimating the magnitude of pathogen-pathogen interactions from clinical specimens is challenging because sampling from symptomatic individuals can create biased estimates. Methods We conducted an observational, cross-sectional study using samples collected by the Seattle Flu Study between 11 November 2018 and 20 August 2021. Samples that tested positive via RT-qPCR for at least one of 17 potential respiratory pathogens were included in this study. Semi-quantitative cycle threshold (Ct) values were used to measure pathogen load. Differences in pathogen load between monoinfected and coinfected samples were assessed using linear regression adjusting for age, season, and recruitment channel. Results 21,686 samples were positive for at least one potential pathogen. Most prevalent were rhinovirus (33.5%), Streptococcus pneumoniae (SPn, 29.0%), SARS-CoV-2 (13.8%) and influenza A/H1N1 (9.6%). 140 potential pathogen pairs were included for analysis, and 56 (40%) pairs yielded significant Ct differences (p < 0.01) between monoinfected and co-infected samples. We observed no virus-virus pairs showing evidence of significant facilitating interactions, and found significant viral load decrease among 37 of 108 (34%) assessed pairs. Samples positive with SPn and a virus were consistently associated with increased SPn load. Conclusions Viral load data can be used to overcome sampling bias in studies of pathogen-pathogen interactions. When applied to respiratory pathogens, we found evidence of viral-SPn facilitation and several examples of viral-viral interference. Multipathogen surveillance is a cost-efficient data collection approach, with added clinical and epidemiological informational value over single-pathogen testing, but requires careful analysis to mitigate selection bias.

Roles for Pathogen Interference in Influenza Vaccination, with Implications to Vaccine Effectiveness (VE) and Attribution of Influenza Deaths

“…Lastly, many studies just focus on respiratory viruses alone thus ignoring the interplay between viruses and bacteria. The key observation is that the detection of common respiratory pathogens in symptomatic individuals remains very low, typically below 30% [ 104 , 112 , 113 , 114 ], which reflects the observations in Section 3 and Table 1 regarding the full range of human pathogens and the role of persistent pathogens—which up to the present has been overlooked in pathogen interference.…”

“…Next comes the method of numerical analysis ranging from simple pathogen-pairs (prevalence yes/no) using various statistical tests [ 103 ], weekly adjustment for the background prevalence of each pathogen pair compared to actual [ 111 ], and a sophisticated multivariate Bayesian framework which included modeling temporal autocorrelation through a hierarchical autoregressive model using the abundance (present yes/no) of the various pathogens [ 112 ], and more recently to the examination of pathogen load rather than just yes/no presence [ 113 ].…”

“…The latest research using pathogen load is that all viruses are mutually adversarial, some more so than others depending on the combination, but that viruses are mutually enhancing of Streptococcus pneumoniae infection [ 113 ].…”

“…Of specific relevance to the potential role of influenza vaccination is the observation that influenza A and B are the most active in inhibiting the load of other viruses [ 113 ]. Hence, influenza vaccination is potentially a powerful agent to promote infection by non-influenza viruses, but should be beneficial against S. pneumoniae infection, except when it opens the way for non-influenza virus infection.…”

Trends in risk factors and symptoms associated with SARS-CoV-2 and Rhinovirus test positivity in King County, Washington: A Test-Negative Design Study of the Greater Seattle Coronavirus Assessment Network

“…[20][21][22][23] Accordingly, we detected more rhinovirus than SARS-CoV-2 infections, but very few other viral pathogens. Another study found that SARS-CoV-2 may repress rhinovirus infection on individual-level data, 52 which may explain the anticorrelation in circulation we observed at the population level. In the past, similar viral interference has been observed between rhinovirus and influenza.…”