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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/12371

Title: A frequentist approach to estimating the force of infection for a respiratory disease using repeated measurement data from a birth cohort
Authors: Mwambi, H.
Ramroop, S.
White, L. J.
Okiro, E. A.
Nokes, D. J.
Shkedy, Ziv
Molenberghs, Geert
Issue Date: 2011
Abstract: This article aims to develop a probability-based model involving the use of direct likelihood formulation and generalised linear modelling (GLM) approaches useful in estimating important disease parameters from longitudinal or repeated measurement data. The current application is based on infection with respiratory syncytial virus. The force of infection and the recovery rate or per capita loss of infection are the parameters of interest. However, because of the limitation arising from the study design and subsequently, the data generated only the force of infection is estimable. The problem of dealing with time-varying disease parameters is also addressed in the article by fitting piecewise constant parameters over time via the GLM approach. The current model formulation is based on that published in White LJ, Buttery J, Cooper B, Nokes DJ and Medley GF. Rotavirus within day care centres in Oxfordshire, UK: characterization of partial immunity. Journal of Royal Society Interface 2008; 5:1481-1490 with an application to rotavirus transmission and immunity.
Notes: [Mwambi, H; Ramroop, S] Univ KwaZulu Natal, Sch Stat & Actuarial Sci, Scottsville, Pmb, South Africa. [White, LJ] Mahidol Oxford Trop Med Res Unit, Bangkok 10400, Thailand. [White, LJ] Univ Oxford, Churchill Hosp, Ctr Trop Med, Nuffield Dept Clin Med,CCVTM, Oxford OX3 7LJ, England. [Okiro, EA; Nokes, DJ] Kenya Govt Med Res Ctr, CGMRC, Kilifi 80108, Kenya. [Nokes, DJ] Univ Warwick, Sch Life Sci, Coventry CV4 7AL, W Midlands, England. [Shkedy, Z; Molenberghs, G] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. MwambiH@ukzn.ac.za
URI: http://hdl.handle.net/1942/12371
DOI: 10.1177/0962280210385749
ISI #: 000296245700008
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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