Branching process models for surveillance of infectious diseases controlled by mass vaccination

Biostatistics. 2003 Apr;4(2):279-95. doi: 10.1093/biostatistics/4.2.279.


Mass vaccination programmes aim to maintain the effective reproduction number R of an infection below unity. We describe methods for monitoring the value of R using surveillance data. The models are based on branching processes in which R is identified with the offspring mean. We derive unconditional likelihoods for the offspring mean using data on outbreak size and outbreak duration. We also discuss Bayesian methods, implemented by Metropolis-Hastings sampling. We investigate by simulation the validity of the models with respect to depletion of susceptibles and under-ascertainment of cases. The methods are illustrated using surveillance data on measles in the USA.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Communicable Diseases / transmission*
  • Computer Simulation
  • Disease Outbreaks / prevention & control
  • Humans
  • Mass Vaccination / methods*
  • Measles / transmission
  • Models, Immunological*
  • Sentinel Surveillance
  • Statistical Distributions
  • Time Factors