Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study.
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.
Type
Journal articleSubject
Bayes TheoremGeography
Humans
Influenza A Virus, H3N2 Subtype
Influenza, Human
Models, Theoretical
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https://hdl.handle.net/10161/6102Published Version (Please cite this version)
10.1371/journal.pcbi.1002835Publication Info
Ratmann, O; Donker, G; Meijer, A; Fraser, C; & Koelle, K (2012). Phylodynamic inference and model assessment with approximate bayesian computation:
influenza as a case study. PLoS Comput Biol, 8(12). pp. e1002835. 10.1371/journal.pcbi.1002835. Retrieved from https://hdl.handle.net/10161/6102.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Katharina V. Koelle
Associate Professor in the Department of Biology
My research focuses on the ecology and evolution of infectious diseases. I use a combination
of mathematical and statistical approaches to understand the processes driving the
disease dynamics of pathogens. My interests include developing models to improve our
understanding of how immune escape and other viral phenotypes impact the ecological
dynamics of RNA viruses, and, in turn, how these ecological dynamics create selection
pressures on viral pathogens. Additional interests include developing
This author no longer has a Scholars@Duke profile, so the information shown here reflects
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