Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data

Proc Biol Sci. 2012 Feb 7;279(1728):444-50. doi: 10.1098/rspb.2011.0913. Epub 2011 Jul 6.


Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes.

MeSH terms

  • Animal Husbandry
  • Animals
  • Chickens
  • Consensus Sequence
  • Ducks
  • Epidemics / veterinary*
  • Hemagglutinins / genetics
  • Humans
  • Influenza A Virus, H7N7 Subtype / genetics
  • Influenza A Virus, H7N7 Subtype / physiology*
  • Influenza in Birds / epidemiology*
  • Influenza in Birds / transmission*
  • Likelihood Functions
  • Markov Chains
  • Monte Carlo Method
  • Netherlands / epidemiology
  • Neuraminidase / genetics
  • RNA-Dependent RNA Polymerase / genetics
  • Sequence Analysis, RNA / veterinary
  • Time Factors
  • Turkeys
  • Viral Proteins / genetics


  • Hemagglutinins
  • PB2 protein, Influenzavirus A
  • Viral Proteins
  • RNA-Dependent RNA Polymerase
  • Neuraminidase