About: A novel framework for inferring parameters of transmission from viral sequence data

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About: A novel framework for inferring parameters of transmission from viral sequence data Goto Sponge NotDistinct Permalink

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	A novel framework for inferring parameters of transmission from viral sequence data
Creator	Illingworthid, Christopher Lumbyid, Casper Nene, Nuno
Source	PMC
abstract	Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.
has issue date	2018-10-16(xsd:dateTime)
bibo:doi	10.1371/journal.pgen.1007718
bibo:pmid	30325921
has license	cc-by
sha1sum (hex)	8a973597ea07d63537723d0324a68fe3759b90ff
schema:url	https://doi.org/10.1371/journal.pgen.1007718
resource representing a document's title	A novel framework for inferring parameters of transmission from viral sequence data
has PubMed Central identifier	PMC6203404
has PubMed identifier	30325921
schema:publication	PLoS Genet
resource representing a document's body	covid:8a973597ea07d63537723d0324a68fe3759b90ff#body_text
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