About: Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models

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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
title	Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models
Creator	Wang, X Wang, Xueying Allen, L Allen, Linda Emerenini, Blessing »more»
source	PMC
abstract	The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. Through mathematical analysis and numerical simulations, we find that the probability of outbreaks increases and time to outbreaks decreases as the prevalence of superspreaders increases in the population. In particular, as disease outbreaks occur more rapidly and more frequently when initiated by superspreaders, our results emphasize the need for expeditious public health interventions.
has issue date	2018-10-25(xsd:dateTime)
bibo:doi	10.1007/978-3-319-98083-6_1
has license	no-cc
sha1sum (hex)	f6202ca147d1ae17734cb820f9c1888848eeb0a2
schema:url	https://doi.org/10.1007/978-3-319-98083-6_1
resource representing a document's title	Searching for Superspreaders: Identifying Epidemic Patterns Associated with Superspreading Events in Stochastic Models
has PubMed Central identifier	PMC7123311
schema:publication	Understanding Complex Biological Systems with Mathematics
resource representing a document's body	covid:f6202ca147d1ae17734cb820f9c1888848eeb0a2#body_text
is schema:about of	named entity 'superspreaders' named entity 'epidemic' named entity 'Middle East respiratory syndrome' named entity 'Superspreaders' named entity 'basic reproduction number' »more»

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