Show simple item record

Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study.

dc.contributor.author Ratmann, Oliver
dc.contributor.author Donker, Gé
dc.contributor.author Meijer, Adam
dc.contributor.author Fraser, Christophe
dc.contributor.author Koelle, Katia
dc.coverage.spatial United States
dc.date.accessioned 2013-01-16T18:20:51Z
dc.date.issued 2012
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/23300420
dc.identifier PCOMPBIOL-D-12-00439
dc.identifier.uri https://hdl.handle.net/10161/6102
dc.description.abstract A key priority in infectious disease research is to understand the ecological and evolutionary drivers of viral diseases from data on disease incidence as well as viral genetic and antigenic variation. We propose using a simulation-based, Bayesian method known as Approximate Bayesian Computation (ABC) to fit and assess phylodynamic models that simulate pathogen evolution and ecology against summaries of these data. We illustrate the versatility of the method by analyzing two spatial models describing the phylodynamics of interpandemic human influenza virus subtype A(H3N2). The first model captures antigenic drift phenomenologically with continuously waning immunity, and the second epochal evolution model describes the replacement of major, relatively long-lived antigenic clusters. Combining features of long-term surveillance data from The Netherlands with features of influenza A (H3N2) hemagglutinin gene sequences sampled in northern Europe, key phylodynamic parameters can be estimated with ABC. Goodness-of-fit analyses reveal that the irregularity in interannual incidence and H3N2's ladder-like hemagglutinin phylogeny are quantitatively only reproduced under the epochal evolution model within a spatial context. However, the concomitant incidence dynamics result in a very large reproductive number and are not consistent with empirical estimates of H3N2's population level attack rate. These results demonstrate that the interactions between the evolutionary and ecological processes impose multiple quantitative constraints on the phylodynamic trajectories of influenza A(H3N2), so that sequence and surveillance data can be used synergistically. ABC, one of several data synthesis approaches, can easily interface a broad class of phylodynamic models with various types of data but requires careful calibration of the summaries and tolerance parameters.
dc.language eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLoS Comput Biol
dc.relation.isversionof 10.1371/journal.pcbi.1002835
dc.subject Bayes Theorem
dc.subject Geography
dc.subject Humans
dc.subject Influenza A Virus, H3N2 Subtype
dc.subject Influenza, Human
dc.subject Models, Theoretical
dc.title Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study.
dc.type Journal article
duke.contributor.id Koelle, Katia|0419074
duke.description.issue 12
duke.description.volume 8
dc.relation.journal PLoS Computational Biology
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/23300420
pubs.begin-page e1002835
pubs.issue 12
pubs.organisational-group Biology
pubs.organisational-group Duke
pubs.organisational-group Global Health Institute
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 8
dc.identifier.eissn 1553-7358


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record