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Eight challenges in phylodynamic inference.
Journal article

Eight challenges in phylodynamic inference.

  • Frost SD Department of Veterinary Medicine, University of Cambridge, Cambridge, UK; Institute of Public Health, University of Cambridge, Cambridge, UK. Electronic address: sdf22@cam.ac.uk.
  • Pybus OG Department of Zoology, University of Oxford, Oxford, UK.
  • Gog JR Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
  • Viboud C Fogarty International Center, National Institutes of Health, Bethesda, USA.
  • Bonhoeffer S Institute of. Integrative Biology, ETH Zurich, Zurich, Switzerland.
  • Bedford T Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
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  • 2015-04-07
Published in:
  • Epidemics. - 2015
Coalescent models
Phylodynamics
Recombination
Selection
Biological Evolution
Communicable Diseases
Genetic Variation
Host-Pathogen Interactions
Humans
Models, Statistical
Phylogeny
Population Dynamics
Recombination, Genetic
Selection, Genetic
Stochastic Processes
English The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/223278
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