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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14250

Title: Towards measles elimination in Italy: Monitoring herd immunity by Bayesian mixture modelling of serological data
Authors: Del Fava, Emanuele
Shkedy, Ziv
Bechini, Angela
Bonanni, Paolo
Manfredi, Piero
Issue Date: 2012
Publisher: ELSEVIER SCIENCE BV
Citation: EPIDEMICS, 4 (3), p. 124-131
Abstract: The analysis of post-vaccination serological data poses nontrivial issues to the epidemiologists and policy makers who want to assess the effects of immunisation programmes. This is especially true for infections on the path to elimination as is the case for measles. We address these problems by using Bayesian Normal mixture models fitted to antibody counts data. This methodology allows us to estimate the seroprevalence of measles by age and, in contrast to conventional methods based on fixed cut-off points, to also distinguish between groups of individuals with different degrees of immunisation. We applied our methodology to two serological samples collected in Tuscany (Italy) in 2003 and in 2005-2006 respectively, i.e., before and after a large vaccination campaign targeted to school-age children. Besides showing the impact of the campaign, we were able to accurately identify a large pocket of susceptible individuals aged about 13-14 in 2005-2006, and a larger group of weakly immune individuals aged about 20 in 2005-2006. These cohorts therefore represent possible targets for further interventions towards measles elimination. (C) 2012 Elsevier B.V. All rights reserved.
Notes: [Del Fava, Emanuele; Shkedy, Ziv] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSt, B-3590 Diepenbeek, Belgium. [Bechini, Angela; Bonanni, Paolo] Univ Florence, Dept Publ Hlth, I-50134 Florence, Italy. [Manfredi, Piero] Univ Pisa, Dept Stat & Math Appl Econ, I-56124 Pisa, Italy.
URI: http://hdl.handle.net/1942/14250
DOI: 10.1016/j.epidem.2012.05.001
ISI #: 000308500800002
ISSN: 1755-4365
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Centre for Statistics
Research Institute Center for Statistics

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