UZH-Logo

Maintenance Infos

Hierarchical modelling of faecal egg counts to assess anthelmintic efficacy


Paul, Michaela; Torgerson, P R; Högland, J; Furrer, Reinhard (2014). Hierarchical modelling of faecal egg counts to assess anthelmintic efficacy. arXiv 1401.2642, University of Zurich.

Abstract

Counting the number of parasite eggs in faecal samples is a widely used diagnostic method to evaluate parasite burden. Typically a sub-sample of the diluted faeces is examined for eggs. The resulting egg counts are multiplied by a specific correction factor to estimate the mean parasite burden. To detect anthelmintic resistance, the mean parasite burden from treated and untreated animals are compared. However, this standard method has some limitations. In particular, the analysis of repeated samples may produce quite variable results as the sampling variability due to the counting technique is ignored. We propose a hierarchical model that takes this sampling variability as well as between-animal variation into account. Bayesian inference is done via Markov chain Monte Carlo sampling. The performance of the hierarchical model is illustrated by a re-analysis of faecal egg count data from a Swedish study assessing the anthelmintic resistance of nematode parasite in sheep. A simulation study shows that the hierarchical model provides better classification of anthelmintic resistance compared to the standard method.

Counting the number of parasite eggs in faecal samples is a widely used diagnostic method to evaluate parasite burden. Typically a sub-sample of the diluted faeces is examined for eggs. The resulting egg counts are multiplied by a specific correction factor to estimate the mean parasite burden. To detect anthelmintic resistance, the mean parasite burden from treated and untreated animals are compared. However, this standard method has some limitations. In particular, the analysis of repeated samples may produce quite variable results as the sampling variability due to the counting technique is ignored. We propose a hierarchical model that takes this sampling variability as well as between-animal variation into account. Bayesian inference is done via Markov chain Monte Carlo sampling. The performance of the hierarchical model is illustrated by a re-analysis of faecal egg count data from a Swedish study assessing the anthelmintic resistance of nematode parasite in sheep. A simulation study shows that the hierarchical model provides better classification of anthelmintic resistance compared to the standard method.

Downloads

9 downloads since deposited on 03 Feb 2015
6 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Working Paper
Communities & Collections:07 Faculty of Science > Institute of Mathematics
05 Vetsuisse Faculty > Chair in Veterinary Epidemiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
510 Mathematics
Language:English
Date:12 January 2014
Deposited On:03 Feb 2015 16:53
Last Modified:05 Apr 2016 18:46
Series Name:arXiv
Funders:SNF, FP7
Free access at:Related URL. An embargo period may apply.
Related URLs:http://arxiv.org/abs/1401.2642
Other Identification Number:arXiv:1401.2642
Permanent URL: https://doi.org/10.5167/uzh-104248

Download

[img]
Preview
Filetype: PDF
Size: 516kB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations