title: Branching process models for surveillance of infectious diseases controlled by mass vaccination.
abstract: 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.
authors.affiliation: Department of Statistics, The Open University, Milton Keynes, MK7 6AA, UK. C.P.Farrington@open.ac.uk
authors.forename: C P
authors.initials: CP
authors.lastname: Farrington
authors.forename: M N
authors.initials: MN
authors.lastname: Kanaan
authors.forename: N J
authors.initials: NJ
authors.lastname: Gay
mesh_terms: D001499:Bayes Theorem; D003141:Communicable Diseases / Q000635:transmission*; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control; D006801:Humans; D032541:Mass Vaccination / Q000379:methods*; D008457:Measles / Q000635:transmission; D018448:Models, Immunological; D018571:Sentinel Surveillance; D016008:Statistical Distributions; D013997:Time Factors