About: Virus–virus interactions impact the population dynamics of influenza and the common cold

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Virus–virus interactions impact the population dynamics of influenza and the common cold
Creator	Johnson, Paul Gunson, Rory Thorburn, Fiona Matthews, Louise Mair, Colette Nickbakhsh, Sema Reeve, Richard Reynolds, Arlene Murcia, Pablo Mcmenamin, James Von Wissmann, Beatrix
Source	Medline; PMC
abstract	The human respiratory tract hosts a diverse community of cocirculating viruses that are responsible for acute respiratory infections. This shared niche provides the opportunity for virus–virus interactions which have the potential to affect individual infection risks and in turn influence dynamics of infection at population scales. However, quantitative evidence for interactions has lacked suitable data and appropriate analytical tools. Here, we expose and quantify interactions among respiratory viruses using bespoke analyses of infection time series at the population scale and coinfections at the individual host scale. We analyzed diagnostic data from 44,230 cases of respiratory illness that were tested for 11 taxonomically broad groups of respiratory viruses over 9 y. Key to our analyses was accounting for alternative drivers of correlated infection frequency, such as age and seasonal dependencies in infection risk, allowing us to obtain strong support for the existence of negative interactions between influenza and noninfluenza viruses and positive interactions among noninfluenza viruses. In mathematical simulations that mimic 2-pathogen dynamics, we show that transient immune-mediated interference can cause a relatively ubiquitous common cold-like virus to diminish during peak activity of a seasonal virus, supporting the potential role of innate immunity in driving the asynchronous circulation of influenza A and rhinovirus. These findings have important implications for understanding the linked epidemiological dynamics of viral respiratory infections, an important step towards improved accuracy of disease forecasting models and evaluation of disease control interventions.
has issue date	2019-12-26(xsd:dateTime)
bibo:doi	10.1073/pnas.1911083116
bibo:pmid	31843887
has license	cc-by
sha1sum (hex)	4461d122183815a09cd253a526f47e0455a5b316
schema:url	https://doi.org/10.1073/pnas.1911083116
resource representing a document's title	Virus–virus interactions impact the population dynamics of influenza and the common cold
has PubMed Central identifier	PMC6936719
has PubMed identifier	31843887
schema:publication	Proc Natl Acad Sci U S A
resource representing a document's body	covid:4461d122183815a09cd253a526f47e0455a5b316#body_text
is schema:about of	named entity 'common cold' named entity 'time series' named entity 'infection' named entity 'common cold virus' named entity 'null hypothesis' named entity 'data sets' named entity 'virus' named entity 'refractory' named entity 'S17' named entity 'observed data' named entity 'p-value' named entity 'virus' named entity 'null hypothesis' named entity 'p-values' named entity 'refractory' named entity 'infection' named entity 'Bayesian hierarchical modelling' named entity 'virus' named entity 'Bivariate' named entity 'randomly permuting' named entity 'null hypothesis' named entity 'Fisher's method' named entity 'quantiles' named entity 'virus' named entity 'infection status' named entity 'p-values' named entity 'time series' named entity 'p-values' named entity 'seasonal influenza' named entity 'virus' named entity 'autocorrelation' named entity 'Correlation coefficients' named entity 'permutation test' named entity 'posterior density' named entity 'virus' named entity 'viruses' named entity 'correction for multiple comparisons' named entity 'virus' named entity 'cross-correlation' named entity 'virus' named entity 'Virus' named entity 'population dynamics' named entity 'INTERACTIONS' named entity 'COMMON COLD' named entity 'IMPACT' named entity 'APPENDIX' named entity 'influenza' named entity 'common cold' named entity 'influenza' named entity 'population dynamics' named entity 'POPULATION DYNAMICS' named entity 'Appendix' named entity 'impact' named entity 'Information' named entity 'VIRUS' named entity 'INFLUENZA' named entity 'SUPPORTING INFORMATION'
is part of of	The human respiratory tract hosts a diverse community of cocirculating viruses that are responsible for acute respiratory infections. This shared niche provides the opportunity for virus–virus interactions which have the potential to affect individual infection risks and in turn influence dynamics of infection at population scales. However, quantitative evidence for interactions has lacked suitable data and appropriate analytical tools. Here, we expose and quantify interactions among respiratory viruses using bespoke analyses of infection time series at the population scale and coinfections at the individual host scale. We analyzed diagnostic data from 44,230 cases of respiratory illness that were tested for 11 taxonomically broad groups of respiratory viruses over 9 y. Key to our analyses was accounting for alternative drivers of correlated infection frequency, such as age and seasonal dependencies in infection risk, allowing us to obtain strong support for the existence of negative interactions between influenza and noninfluenza viruses and positive interactions among noninfluenza viruses. In mathematical simulations that mimic 2-pathogen dynamics, we show that transient immune-mediated interference can cause a relatively ubiquitous common cold-like virus to diminish during peak activity of a seasonal virus, supporting the potential role of innate immunity in driving the asynchronous circulation of influenza A and rhinovirus. These findings have important implications for understanding the linked epidemiological dynamics of viral respiratory infections, an important step towards improved accuracy of disease forecasting models and evaluation of disease control interventions. covid:4461d122183815a09cd253a526f47e0455a5b316#body_text Virus–virus interactions impact the population dynamics of influenza and the common cold

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