Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24174
Title: Estimating age-time-dependent malaria force of infection accounting for unobserved heterogeneity
Authors: MUGENYI, Levicatus 
ABRAMS, Steven 
HENS, Niel 
Issue Date: 2017
Source: EPIDEMIOLOGY AND INFECTION, 145(12), p. 2545-2562
Abstract: Despite well-recognized heterogeneity in malaria transmission, key parameters such as the force of infection (FOI) are generally estimated ignoring the intrinsic variability in individual infection risks. Given the potential impact of heterogeneity on the estimation of the FOI, we estimate this quantity accounting for both observed and unobserved heterogeneity. We used cohort data of children aged 0·5-10 years evaluated for the presence of malaria parasites at three sites in Uganda. Assuming a Susceptible-Infected-Susceptible model, we show how the FOI relates to the point prevalence, enabling the estimation of the FOI by modelling the prevalence using a generalized linear mixed model. We derive bounds for varying parasite clearance distributions. The resulting FOI varies significantly with age and is estimated to be highest among children aged 5-10 years in areas of high and medium malaria transmission and highest in children aged below 1 year in a low transmission setting. Heterogeneity is greater between than within households and it increases with decreasing risk of malaria infection. This suggests that next to the individual's age, heterogeneity in malaria FOI may be attributed to household conditions. When estimating the FOI, accounting for both observed and unobserved heterogeneity in malaria acquisition is important for refining malaria spread models.
Notes: [Mugenyi, L.] Infect Dis Res Collaborat, Plot 2C Nakasero Hill Rd, Kampala, Uganda. [Mugenyi, L.; Abrams, S.; Hens, N.] UHasselt Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Ctr Stat, Diepenbeek, Belgium. [Hens, N.] Univ Antwerp, Vaccine & Infect Dis Inst, Ctr Hlth Econ Res & Modelling Infect Dis, Antwerp, Belgium.
Keywords: clearance rate distribution; SIS compartmental model; generalized linear mixed model; point prevalence
Document URI: http://hdl.handle.net/1942/24174
ISSN: 0950-2688
e-ISSN: 1469-4409
DOI: 10.1017/S0950268817001297
ISI #: 000414606100015
Rights: © Cambridge University Press 2017
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

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