Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus
6 affiliations
- 1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
- 2High Meadows Environmental Institute, Princeton University, Princeton, New Jersey, United States of America
- 3School of Biological Sciences, Seoul National University, Seoul, Korea
- 4Institute for Data Innovation in Science, Seoul National University, Seoul, Korea
- 5Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- 6Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America
Abstract
Many respiratory pathogens co-circulate within human populations. Yet, how pathogen community structure shapes the dynamics of infectious diseases remains poorly understood. At the population level, investigating polymicrobial dynamics, with potential underlying competitive or cooperative interactions, is challenging, because of confounding factors such as differing seasonality. This is particularly true for endemic pathogens which typically exhibit stable periodic dynamics. Their disruption due to the implementation of non-pharmaceutical interventions during the COVID-19 pandemic thus represents a unique large-scale natural experiment that can be leveraged to provide valuable insights into the complex interplay between respiratory pathogens. Here, we focus on the population dynamics of human rhinovirus (common cold) and on the potential viral interference of influenza A virus (flu A), which is hypothesized to account for their asynchronous circulation. Using a Bayesian framework, we first show based on simulations that exogenous perturbations can be a powerful tool to disentangle the contribution of pathogen interaction from other epidemiological factors. We then apply our framework to surveillance time series from the US and Canada spanning the COVID-19 pandemic. We estimate key parameters of rhinovirus but find no conclusive support for an influence of influenza A virus at the population level. Author summary: A wide range of respiratory viruses (influenza viruses, coronaviruses, respiratory syncytial virus, ...) circulate in the same communities and can infect the same individuals. Yet, the extent to which these pathogens interact and shape each other’s spread remains under-explored and represents an emerging frontier in public health. We focus here on the dynamics of human rhinovirus, a leading cause of the common cold, particularly among young children. Rhinovirus and influenza A virus typically peak at different times of the year, raising the possibility of some negative interactions. For viruses that peak at consistent times each year, it is however challenging to determine whether differences in their timing are due to true virus-virus interactions or simply reflect their own, independent, seasonal trends. We develop a mathematical modeling approach that leverages the disruption of these dynamics due to the COVID-19 pandemic as a large-scale natural experiment. Our work demonstrates how such epidemiological perturbations can be used to uncover interactions among co-circulating pathogens. Using multi-year surveillance incidence data from the United States and Canada, we estimate key parameters governing rhinovirus transmission and immunity, but find no substantial impact of influenza A virus.
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BibTeX
@article{benhamou2026leveraging,
title = {{Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus}},
author = {Wakinyan Benhamou and Emily Howerton and Sang Woo Park and Cécile Viboud and C. Jessica E. Metcalf and Bryan T. Grenfell},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014784},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014784},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus
AU - Wakinyan Benhamou
AU - Emily Howerton
AU - Sang Woo Park
AU - Cécile Viboud
AU - C. Jessica E. Metcalf
AU - Bryan T. Grenfell
PY - 2026
DA - 2026/09/11
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014784
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014784
AB - Many respiratory pathogens co-circulate within human populations. Yet, how pathogen community structure shapes the dynamics of infectious diseases remains poorly understood. At the population level, investigating polymicrobial dynamics, with potential underlying competitive or cooperative interactions, is challenging, because of confounding factors such as differing seasonality. This is particularly true for endemic pathogens which typically exhibit stable periodic dynamics. Their disruption due to the implementation of non-pharmaceutical interventions during the COVID-19 pandemic thus represents a unique large-scale natural experiment that can be leveraged to provide valuable insights into the complex interplay between respiratory pathogens. Here, we focus on the population dynamics of human rhinovirus (common cold) and on the potential viral interference of influenza A virus (flu A), which is hypothesized to account for their asynchronous circulation. Using a Bayesian framework, we first show based on simulations that exogenous perturbations can be a powerful tool to disentangle the contribution of pathogen interaction from other epidemiological factors. We then apply our framework to surveillance time series from the US and Canada spanning the COVID-19 pandemic. We estimate key parameters of rhinovirus but find no conclusive support for an influence of influenza A virus at the population level. Author summary: A wide range of respiratory viruses (influenza viruses, coronaviruses, respiratory syncytial virus, ...) circulate in the same communities and can infect the same individuals. Yet, the extent to which these pathogens interact and shape each other’s spread remains under-explored and represents an emerging frontier in public health. We focus here on the dynamics of human rhinovirus, a leading cause of the common cold, particularly among young children. Rhinovirus and influenza A virus typically peak at different times of the year, raising the possibility of some negative interactions. For viruses that peak at consistent times each year, it is however challenging to determine whether differences in their timing are due to true virus-virus interactions or simply reflect their own, independent, seasonal trends. We develop a mathematical modeling approach that leverages the disruption of these dynamics due to the COVID-19 pandemic as a large-scale natural experiment. Our work demonstrates how such epidemiological perturbations can be used to uncover interactions among co-circulating pathogens. Using multi-year surveillance incidence data from the United States and Canada, we estimate key parameters governing rhinovirus transmission and immunity, but find no substantial impact of influenza A virus.
SN - 1553-7358
ER -