About: Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics

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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	Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics
Creator	Egli, Adrian Battegay, Manuel Bedford, Trevor Syedbasha, Mohammedyaseen Hadfield, James Neher, Richard Ritz, Nicole Brunner, Myrta Buser, Andreas Dubuis, Olivier Goldman, Nina Hollenstein, Yvonne Lang, Brian Lang, Daniela Müller, Nicola Saalfrank, Claudia Sailer, Nadine Stadler, Tanja Wüthrich, Daniel Zeller, Andreas Nickel, Christian Augustin, Noémi Mb, Helena Naegele, Michael Schneider-Sliwa, Rita
Source	BioRxiv
abstract	Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, it’s local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. We find trends in transmission dynamics correlated positively with trends in temperature, but not relative humidity nor school holidays. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network, while school children likely drove the spread within the remaining transmission network. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities. Author summary As shown with the current SARS-CoV-2 pandemic, respiratory diseases can quickly spread around the globe. While it can be hugely important to understand how diseases spread around the globe, local spread is most often the main driver of novel infections of respiratory diseases such as SARS-CoV-2 or influenza. We here use genetic sequence data alongside patient information to better understand what the drives the local spread of influenza by looking at the 2016/2017 influenza season in Basel, Switzerland as an example. The genetic sequence data allows us to reconstruct the how the transmission dynamics changed over the course of the season, which we correlate to changes, but not humidity or school holidays. Additionally, the genetic sequence data allows us to see how individual cases are connected. Using patient information, such as age and household status our analyses suggest that the elderly mainly transmit within their own transmission network. Additionally, they suggest that school aged children, but not pre-school aged children are important drivers of the local spread of influenza.
has issue date	2020-04-28(xsd:dateTime)
bibo:doi	10.1101/2020.04.27.052225
has license	biorxiv
sha1sum (hex)	0f24d0303ff578e21348ed633876175ad11c82a3
schema:url	https://doi.org/10.1101/2020.04.27.052225
resource representing a document's title	Characterising the epidemic spread of Influenza A/H3N2 within a city through phylogenetics
schema:publication	bioRxiv
resource representing a document's body	covid:0f24d0303ff578e21348ed633876175ad11c82a3#body_text
is schema:about of	named entity 'transmitted' named entity 'elderly' named entity 'INFLUENZA VIRUSES' named entity 'LOCAL TRANSMISSION ' named entity 'ELDERLY' named entity 'INFECTING' named entity 'sequenced' named entity 'hospitals' named entity 'global population' named entity 'The' named entity 'plan' named entity 'large' named entity 'While' named entity 'cases' named entity 'Switzerland' named entity 'scale' named entity 'season' named entity 'extent' named entity 'reveal' named entity 'global population' named entity 'Switzerland' named entity 'influenza' named entity 'influenza season' named entity 'local transmission' named entity 'seasonal influenza' named entity 'transmission network' named entity 'influenza' named entity 'repeated' named entity 'reproduction number' named entity 'sampling' named entity 'nucleotide' named entity 'Basel' named entity 'Basel' named entity 'influenza' named entity 'France' named entity 'evolution' named entity 'probability' named entity 'confidence intervals' named entity 'Basel' named entity 'New York' named entity 'absolute humidity' named entity 'USB' named entity 'confidence intervals' named entity 'Basel' named entity 'influenza transmission' named entity 'Probability' named entity 'Viral shedding' named entity 'Basel' named entity 'epidemic spread' named entity 'influenza' named entity 'negative binomial' named entity 'Basel' named entity 'influenza' named entity 'Basel' named entity 'strong mixing' named entity 'MCMC' named entity 'Tukey method' named entity 'seasonal influenza' named entity 'epidemic' named entity 'influenza'

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