Evolutionary analysis of the dynamics of viral infectious disease

Pybus OG & Rambaut A

(2009) Nature Reviews Genetics 10, 540-550.

Many organisms that cause infectious diseases, particularly RNA viruses, mutate so rapidly that their evolutionary and ecological behaviours are inextricably linked. Consequently, aspects of the transmission and epidemiology of these pathogens are imprinted on the genetic diversity of their genomes. Large-scale empirical analyses of the evolutionary dynamics of important pathogens are now feasible owing to the increasing availability of pathogen sequence data and the development of new computational and statistical methods of analysis. In this Review, we outline the questions that can be answered using viral evolutionary analysis across a wide range of biological scales.


  • The rapid evolution of many pathogens, particularly RNA viruses, means that their evolution and ecology occur on the same timescale, and therefore must be studied jointly to be fully understood.
  • The rapid growth in gene sequence data and the development of new analysis techniques has enabled researchers to study the evolutionary dynamics of important human pathogens such as HIV, influenza, hepatitis C and dengue virus. The term phylodynamics has come to be associated with such studies.
  • Phylodynamic questions arise in a number of practical contexts, including epidemic surveillance, outbreak control, forensics and clinical medicine.
  • Evolutionary analysis methods can be applied to the investigation of viral dynamics at different organizational scales, from global studies of pathogen dissemination among continents, to the dynamics of infection within the tissues of individual infected hosts.
  • Viral genomes are an important and independent source of information about epidemiological processes, thereby supporting and corroborating epidemiological results obtained using standard surveillance methods.
  • The introduction of next-generation sequencing technologies will greatly increase the amount of viral genetic data available for analysis. Substantial changes and improvements to analysis methodologies will be necessary to deal with this exciting change.

Andrew Rambaut, 2007