Skip Navigation

This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Farrington, C. P.
Right arrow Articles by Gay, N. J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Farrington, C. P.
Right arrow Articles by Gay, N. J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Biostatistics 4:279-295 (2003)
© 2003 Oxford University Press

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

C. P. Farrington*, M. N. Kanaan and N. J. Gay

Department of Statistics, The Open University, Milton Keynes, MK7 6AA, UK C.P.Farrington{at}open.ac.uk
Department of Epidemiology and Population Health, The American University of Beirut, PO Box 11-0236, Riad El Solh 11072020, Beirut, Lebanon
Communicable Diseases Surveillance Centre, 61 Colindale Avenue, London, NW9 5EQ, UK

*To whom correspondence should be addressed

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.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Proc R Soc BHome page
A. Yates, R. Antia, and R. R Regoes
How do pathogen evolution and host heterogeneity interact in disease emergence?
Proc R Soc B, December 22, 2006; 273(1605): 3075 - 3083.
[Abstract] [Full Text] [PDF]

Home page
 Proc R Soc BHome page
N. C Grassly and C. Fraser
Seasonal infectious disease epidemiology
Proc R Soc B, October 7, 2006; 273(1600): 2541 - 2550.
[Abstract] [Full Text] [PDF]

Home page
 Am J EpidemiolHome page
S. Cauchemez, P.-Y. Boelle, G. Thomas, and A.-J. Valleron
Estimating in Real Time the Efficacy of Measures to Control Emerging Communicable Diseases
Am. J. Epidemiol., September 15, 2006; 164(6): 591 - 597.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
B. S. Cooper, G. F. Medley, S. P. Stone, C. C. Kibbler, B. D. Cookson, J. A. Roberts, G. Duckworth, R. Lai, and S. Ebrahim
Methicillin-resistant Staphylococcus aureus in hospitals and the community: Stealth dynamics and control catastrophes
PNAS, July 6, 2004; 101(27): 10223 - 10228.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.