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.

Figure 1:

Ct distribution across age groups for each pathogen. Large dots represent mean values, each small dot represents the Ct value for one sample. Ct values range up to a maximum cycle of 28, except for SARS-CoV-2, for which the Taqman qPCR assay used ranged up to 40. Note that 146 (8·6% of total) SARS-CoV-2 Crt observations made using the OpenArray assay are not shown here to maintain consistency. We note that numerical Ct comparisons are meant to be within a target (across age) and not between targets.

Figure 2:

Total positive samples by week over the study period (top), and positive samples per week as a percentage of total weekly positive samples (bottom).

Figure 3:

Adjusted Ct difference associated with each pathogen-pathogen pair, as well as average effects over all viruses (shown on margins). Colors reflect differences in Ct, with blues indicating an increase associated with interaction (suggesting interference), and reds indicating a decrease (suggesting facilitation). The large number in each square represents the average difference in Ct for mono- versus co-infections. The small number at the corner of each square represents the number of co-infected specimens in the sample. Stars (*) represent statistically significant Ct differences (p < 0·01). Pairs with fewer than 10 mono- or co-infected specimens were excluded. The first column and bottom row represent the average effects over all viruses. Full regression results summarized in this figure are available in the Supplementary Data.