Open access
Date
2011-12Type
- Review Article
Abstract
Epidemic modeling of infectious diseases has a long history in both theoretical and empirical research. However the recent explosion of genetic data has revealed the rapid rate of evolution that many populations of infectious agents undergo and has underscored the need to consider both evolutionary and ecological processes on the same time scale. Mathematical epidemiology has applied dynamical models to study infectious epidemics, but these models have tended not to exploit – or take into account – evolutionary changes and their effect on the ecological processes and population dynamics of the infectious agent. On the other hand, statistical phylogenetics has increasingly been applied to the study of infectious agents. This approach is based on phylogenetics, molecular clocks, genealogy-based population genetics and phylogeography. Bayesian Markov chain Monte Carlo and related computational tools have been the primary source of advances in these statistical phylogenetic approaches. Recently the first tentative steps have been taken to reconcile these two theoretical approaches. We survey the Bayesian phylogenetic approach to epidemic modeling of infection diseases and describe the contrasts it provides to mathematical epidemiology as well as emphasize the significance of the future unification of these two fields. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000086505Publication status
publishedExternal links
Journal / series
Infection, Genetics and EvolutionVolume
Pages / Article No.
Publisher
ElsevierSubject
Coalescent; Phylodynamics; Statistical phylogeography; Phylogenetic epidemiology; Rapidly evolving viruses; Stochastic SIROrganisational unit
03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
09490 - Stadler, Tanja / Stadler, Tanja
Notes
Received 1 April 2011, Revised 9 August 2011, Accepted 9 August 2011, Available online 31 August 2011.More
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