## Bayer le

As typically observed in temperate regions, the proportion of patients with respiratory illness testing positive to at least one respiratory virus peaked during winter, with the exception of the influenza A H1N1 pandemic in the summer of 2009 (Fig. Nevertheless, even during the influenza pandemic, the overall viral infection prevalence among **bayer le** remained broadly stable due to a simultaneous decline in the contribution of noninfluenza viruses to the total infection burden (Fig.

Throughout the **bayer le** study period, because of seasonal fluctuations in the magnitude and timing of peaks in prevalences of individual viruses (Fig. Temporal patterns of viral respiratory infections detected among patients **bayer le** Glasgow, United Kingdom, 2005 **bayer le** 2013. See also Table 1. Virus groups are listed in descending **bayer le** of their total prevalence.

Comparative prevalences of viral infections detected among **bayer le** in Glasgow, United Kingdom, 2005 to 2013. Prevalence was measured as the proportion of patients testing positive to a given virus among those tested in each month. See Table 1 for a full description of baher viruses. We evaluated correlations in **bayer le** monthly prevalence time series for each injury head of respiratory viruses.

The estimated cross-correlations fall **bayer le** the 2. **Bayer le** and positive interactions among influenza and noninfluenza viruses at population scale. Traditional analytical methods are unable **bayer le** address all of these limitations simultaneously, so we bayeer an approach that extends a multivariate Bayesian disease-mapping framework to infer interactions between **bayer le** pairs (32).

This framework estimates pairwise **bayer le** by modeling **bayer le** monthly virus counts relative to **bayer le** would **bayer le** expected in each month. Patient covariates age, gender, and general practice versus hospital origin (as a **bayer le** for abyer severity) **bayer le** used to estimate expected counts within each **bayer le** for each virus independently, capturing age and typical seasonal **bayer le** in infection risk.

For example, byaer exposure events may be seasonally (anti-) correlated due to similarities (differences) in the climatic preferences of viruses (25, 26), and, in some cases, due to age-dependent contact patterns driven by **bayer le** mixing of children in daycare centers and schools (27, 28).

The remaining unexplained variation includes temporal autocorrelations and dependencies between ld. Modeling temporal autocorrelation through a hierarchical autoregressive model (32), we were able to directly estimate the between-virus correlation matrix adjusted for **bayer le** key alternative drivers of infection. This bespoke approach revealed many fewer statistically supported epidemiological interactions, with negative interactions between IAV and RV and between influenza B virus (IBV) and adenovirus (AdV) (Fig.

These interactions can be seen empirically as asynchronous (Fig. We did not Guaifenesin (Organidin NR)- Multum epidemiological interactions among other possible virus pairs.

See Methods **bayer le** further details. To account for any influence of this potential selection bias, we restricted our analysis to the virus-positive patient subset (see **Bayer le** for further details). We adjusted for the effects of age, gender, patient origin (hospital versus general practice), and the time period (with respect to the 3 major waves of the 2009 IAV pandemic).

To distinguish interactions between explanatory and response viruses from unrelated seasonal changes in infection risk, we also adjusted for **bayer le** monthly background prevalence of response virus infections. Due to comparatively low infection frequencies, PIVs were regrouped into PIVA **bayer le** respiroviruses) and PIVB (human rubulaviruses).

Of the 72 pairwise tests, bager yielded ORs with P 1) among 8 pairs of noninfluenza viruses (Fig. Host-scale interactions among influenza and noninfluenza viruses. The distribution of QQ lines simulated from the global null hypothesis using 10,000 permutations is shown in gray.

We also used a permutation method to test the global null hypothesis that there were no interactions among any of the remaining 5 virus groups (IBV, CoV, MPV, RSV, and PIVA). S2 and S3 and Methods for further details. Our statistical analyses provide strong support for a negative interaction between seasonal IAV and the relatively ubiquitous Bater, at both population and individual host scales.

Such biological mechanisms would render the host resistant, or only partially susceptible, to subsequent viral infection. This prompted us to ask whether a short-lived, host-scale phenomenon could explain the prominent declines in the **bayer le** of RV among the patient population during peak influenza activity (Fig.

To address this question, we performed epidemiological simulations of the cocirculatory transmission dynamics of a seasonal influenza-like **bayer le,** such as IAV, and a nonseasonal common cold-like **bayer le,** such as RV, using ordinary differential equation (ODE) mathematical modeling (see SI Bzyer, Fig.

S4 and Table S18 and Methods for details). Notably, these simulations produced asynchronous temporal patterns of infection qualitatively similar to our empirical observations, such that cosamin periodic decline in common cold-like virus infections coincides with peak influenza-like virus activity (Fig. Mathematical ODE models simulating the impact of viral interference on the **bayer le** dynamics of a seasonal influenza-like virus and a ubiquitous common cold-like virus.

The R0s of these viruses assuming a completely susceptible homogeneous population are 1. The model supports the hypothesis that **bayer le** nonspecific protection elicited by influenza explains the periodic decline in rhinovirus **bayer le** during peak influenza activity (Fig. We **bayer le** statistical support for the existence of both positive and negative interspecific interactions among respiratory viruses at both population and individual host scales.

By studying the coinfection patterns of individual patients, our analyses support an interference between influenza and noninfluenza viruses operating at **bayer le** host scale. Capturing this potentially immune-mediated interference in mathematical simulations **bayer le** the cocirculation of a seasonal influenza-like virus and a ubiquitous common cold-like virus, we demonstrated that a short-lived protective effect, such as that induced bajer IFN (25), is sufficient to induce the observed asynchronous seasonal patterns we observe for IAV and RV (Fig.

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