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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/431

Title: Modeling forces of infection using monotone local polynomials
Authors: Shkedy, Ziv
Aerts, Marc
Molenberghs, Geert
Beutels, Phillipe
Van Damme, Pierre
Keywords: Infectious diseases
Issue Date: 2003
Publisher: BLACKWELL PUBLISHING
Citation: Journal of the Royal Statistical Society: series C: applied statistics, 52(4). p. 469-485
Abstract: On the basis of serological data from prevalence studies of rubella, mumps and hepatitis A, the paper describes a flexible local maximum likelihood method for the estimation of the rate at which susceptible individuals acquire infection at different ages. In contrast with parametric models that have been used before in the literature, the local polynomial likelihood method allows this age-dependent force of infection to be modelled without making any assumptions about the parametric structure. Moreover, this method allows for simultaneous nonparametric estimation of age-specific incidence and prevalence. Unconstrained models may lead to negative estimates for the force of infection at certain ages. To overcome this problem and to guarantee maximal flexibility, the local smoother can be constrained to be monotone. It turns out that different parametric and nonparametric estimates of the force of infection can exhibit considerably different qualitative features like location and the number of maxima, emphasizing the importance of a well-chosen flexible statistical model.
URI: http://hdl.handle.net/1942/431
DOI: 10.1111/1467-9876.00418
ISI #: 000186031800007
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Validation: ecoom, 2004
Appears in Collections: Research publications

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