About: A data-driven model for influenza transmission incorporating media effects

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A data-driven model for influenza transmission incorporating media effects
Creator	Mitchell, Lewis Ross, Joshua
Source	Medline; PMC
abstract	Numerous studies have attempted to model the effect of mass media on the transmission of diseases such as influenza; however, quantitative data on media engagement has until recently been difficult to obtain. With the recent explosion of ‘big data’ coming from online social media and the like, large volumes of data on a population’s engagement with mass media during an epidemic are becoming available to researchers. In this study, we combine an online dataset comprising millions of shared messages relating to influenza with traditional surveillance data on flu activity to suggest a functional form for the relationship between the two. Using this data, we present a simple deterministic model for influenza dynamics incorporating media effects, and show that such a model helps explain the dynamics of historical influenza outbreaks. Furthermore, through model selection we show that the proposed media function fits historical data better than other media functions proposed in earlier studies.
has issue date	2016-10-26(xsd:dateTime)
bibo:doi	10.1098/rsos.160481
bibo:pmid	27853563
has license	cc-by
sha1sum (hex)	a106bdc3abdca61cda30b7f968c388e320e547e3
schema:url	https://doi.org/10.1098/rsos.160481
resource representing a document's title	A data-driven model for influenza transmission incorporating media effects
has PubMed Central identifier	PMC5098988
has PubMed identifier	27853563
schema:publication	R Soc Open Sci
resource representing a document's body	covid:a106bdc3abdca61cda30b7f968c388e320e547e3#body_text
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