Optimizing denominator data estimation through a multimodel approach

Bryssinckx, Ward and Ducheyne, Els and Versteirt, Veerle and Leirs, Herwig and Hendrickx, Guy (2014) Optimizing denominator data estimation through a multimodel approach. Geospatial Health, 8 (2). pp. 573-582. ISSN 1970-7096

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To assess the risk of (zoonotic) disease transmission in developing countries, decision makers generally rely on distribution estimates of animals from survey records or projections of historical enumeration results. Given the high cost of large-scale surveys, the sample size is often restricted and the accuracy of estimates is therefore low, especially when spatial high-resolution is applied. This study explores possibilities of improving the accuracy of livestock distribution maps without additional samples using spatial modelling based on regression tree forest models, developed using subsets of the Uganda 2008 Livestock Census data, and several covariates. The accuracy of these spatial models as well as the accuracy of an ensemble of a spatial model and direct estimate was compared to direct estimates and “true” livestock figures based on the entire dataset. The new approach is shown to effectively increase the livestock estimate accuracy (median relative error decrease of 0.166-0.037 for total sample sizes of 80-1,600 animals, respectively). This outcome suggests that the accuracy levels obtained with direct estimates can indeed be achieved with lower sample sizes and the multimodel approach presented here, indicating a more efficient use of financial resources

Item Type: Article
Uncontrolled Keywords: Spatial modelling, survey design, extensive livestock systems, multimodel models, Uganda
Subjects: 600 Tecnologia - Scienze applicate > 630 Agricoltura e tecnologie connesse > 636 Allevamento di animali > 636.089 Scienze veterinarie, Medicina veterinaria
Depositing User: Chiara D'Arpa
Date Deposited: 29 Jul 2014 14:04
Last Modified: 29 Jul 2014 15:03
URI: http://eprints.bice.rm.cnr.it/id/eprint/9628

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