About: Inferring pandemic growth rates from sequence data

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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	Inferring pandemic growth rates from sequence data
Creator	Fraser, Christophe Ferguson, Neil De Silva, Eric
Source	Medline; PMC
abstract	Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.
has issue date	2012-08-07(xsd:dateTime)
bibo:doi	10.1098/rsif.2011.0850
bibo:pmid	22337627
has license	cc-by
sha1sum (hex)	72ddf8d0c71b8b1ece743904f2acd2c1a00984c4
schema:url	https://doi.org/10.1098/rsif.2011.0850
resource representing a document's title	Inferring pandemic growth rates from sequence data
has PubMed Central identifier	PMC3385754
has PubMed identifier	22337627
schema:publication	J R Soc Interface
resource representing a document's body	covid:72ddf8d0c71b8b1ece743904f2acd2c1a00984c4#body_text
is schema:about of	named entity 'APPLIED' named entity 'EFFECTIVE POPULATION SIZE' named entity 'REAL' named entity 'ROLE' named entity 'INFLUENZA' named entity 'FALSE' named entity 'NON-' named entity 'EPIDEMIC' named entity 'INCREASING' named entity 'LARGE' named entity 'RECENT' named entity 'SIMULATIONS' named entity 'LIKELY' named entity 'USING' named entity 'REPRESENT' named entity 'OUTBREAKS' named entity 'USED' named entity 'UNCONTROLLED' named entity 'PANDEMIC' named entity 'SLOWING' named entity 'POPULATION SIZE' named entity 'DATA' named entity 'COALESCENT' named entity 'H1N1' named entity 'SAMPLING' named entity 'APPLICATION' named entity 'CORRECT' named entity 'BUT' named entity 'BEAST' named entity 'PROMINENT' named entity 'ANALYSIS' named entity 'CASES' named entity 'DENSITY' named entity 'RANGE' named entity 'TIME' named entity 'EXACERBATE' named entity 'ABILITY' named entity 'INFERRING' named entity 'SEQUENCE DATA' named entity 'DIAGNOSTIC INDICATOR' named entity 'CLUSTERS' named entity 'SUGGEST' named entity 'POPULATION DYNAMICS' named entity 'GROWING' named entity 'HERE' covid:arg/72ddf8d0c71b8b1ece743904f2acd2c1a00984c4 named entity 'BASED' named entity 'DYNAMICS' named entity 'BIASES' named entity 'PRESENT' named entity 'POOR' named entity 'INFERENCE' named entity 'INFECTED POPULATION' named entity 'INFER' named entity 'HAVE' named entity 'PANDEMIC' named entity 'GROWTH RATES' named entity 'ESTIMATES' named entity 'SIMULATED' named entity 'COMMON' named entity 'THESE' named entity 'PACKAGE' named entity 'REDUCE' named entity 'SCENARIOS' named entity 'PLAYING' named entity 'ESTIMATION' named entity 'TO TEST' named entity 'LEADS' named entity 'PERIODS' named entity 'METHODS' named entity 'GENERIC' named entity 'HOW' named entity 'SEQUENCE DATA' named entity 'CONSIDER' named entity 'EASILY' named entity 'PAST' named entity 'RECONSTRUCTED' named entity 'OUR' named entity 'SPREAD'

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