Does the Flu Keep the Common Cold in Check?

Review written by: Megan Mauriello

Investigating polymicrobial dynamics is complicated due to many confounding factors, including differing seasonality. Disruption due to non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic can serve as a large-scale natural experiment. Rhinoviruses (Enterovirus spp., Picornaviridae) are some of the causal agents of the common cold, and are non-enveloped, +ssRNA viruses including multiple species and subtypes. Influenza A viruses (Influenza A Virus spp., Orthomyxoviridae) are enveloped, multisegmented -ssRNA viruses. Rhinoviruses and influenza A viruses each follow their own distinct seasonality patterns, and there are hypotheses that human rhinoviruses and influenza A viruses have interfering interactions, but previous studies have inconclusive results on if an interference exists, and the directionality of that interference. Should they exist, interferences could be the result of individual immunological interactions, or ecological interactions.

A Bayesian framework was used to model population dynamics of human enteroviruses (RV), with influenza A viruses (IAV) detection as an external input variable. Rapid testing does not differentiate rhinoviruses from other Enterovirus spp., so enterovirus monitoring was used as a proxy for rhinovirus circulation, as most detected Enterovirus spp. are expected to be rhinoviruses. The model was first tested with simulations with varying parameters; then, the framework was applied to surveillance data from the United States and Canada pre- and (post-)pandemic  to ask the question: Is the persistence of RV during the COVID-19 pandemic enhanced by the absence of IAV?

Figure. Using simulated outbreaks, the authors showed that disruptions like pandemic lockdowns make it possible to tell whether flu suppresses the common cold. Without disruptions, that effect can be hidden by ordinary seasonal patterns.

This modeling was able to estimate several parameters of RV transmission and immunity, including the direction and intensity of RV interaction due to IAV (ϕ), mean duration of immune protection (Ω), and basic (R) and effective (R₀) reproduction numbers. The model did not, however, support interference by RV on IAV at the population level (ϕ≈0).

Exogenous epidemiological perturbations, such as NPIs, can help parse out different parameters of polymicrobial dynamics. Although the model did not support interference by RV on IAV, the reverse was not intensively tested due to limitations of modeling IAV dynamics. Regardless, this framework could be applied to other polymicrobial interactions, treating them as bipartite to test various hypotheses.

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READ MORE: Benhamou W, Howerton E, Park SW, Viboud C, Metcalf CJE, Grenfell BT. Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus. PLoS Comput Biol. 2026;22(9):e1014784. https://doi.org/10.1371/journal.pcbi.1014784