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Modelling the geographical distribution of co-infection risk from single-disease surveys

Schur, N. and Gosoniu, L. and Raso, G. and Utzinger, J. and Vounatsou, P.. (2011) Modelling the geographical distribution of co-infection risk from single-disease surveys. Statistics in medicine, Vol. 30, H. 14. pp. 1761-1767.

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Official URL: http://edoc.unibas.ch/dok/A6002274

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Abstract

Background: The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data. Methods: Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Cote d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques. Results: The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data. Conclusions: In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases. Copyright (c) 2011 John Wiley & Sons, Ltd
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Infectious Disease Modelling > Infectious Disease Modelling (Smith)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Eco System Health Sciences > Health Impact Assessment (Utzinger)
UniBasel Contributors:Vounatsou, Penelope and Utzinger, Jürg and Raso, Giovanna
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Wiley
ISSN:0277-6715
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:08 Nov 2012 16:23
Deposited On:08 Nov 2012 16:20

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