Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10975
Title: Seventy-five years of estimating the force of infection from current status data
Authors: HENS, Niel 
AERTS, Marc 
FAES, Christel 
SHKEDY, Ziv 
Lejeune, O.
van Damme, P.
Beutels, P.
Issue Date: 2010
Publisher: CAMBRIDGE UNIV PRESS
Source: EPIDEMIOLOGY AND INFECTION, 138 (6). p. 802-812
Abstract: The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.
Notes: [Hens, N.; Aerts, M.; Faes, C.; Shkedy, Z.] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Hens, N.; Lejeune, O.; van Damme, P.; Beutels, P.] Univ Antwerp, Ctr Hlth Econ Res & Modelling Infect Dis, B-2020 Antwerp, Belgium. [Hens, N.; Lejeune, O.; van Damme, P.; Beutels, P.] Univ Antwerp, Ctr Evaluat Vaccinat, Vaccine & Infect Dis Inst, B-2020 Antwerp, Belgium. niel.hens@uhasselt.be
Keywords: Hugo Muench; incidence data; infectious diseases; serological data; statistical modelling;Hugo Muench; incidence data; infectious diseases; serological data; statistical modelling
Document URI: http://hdl.handle.net/1942/10975
ISSN: 0950-2688
e-ISSN: 1469-4409
DOI: 10.1017/S0950268809990781
ISI #: 000278177800002
Category: A1
Type: Journal Contribution
Validations: ecoom 2011
Appears in Collections:Research publications

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