Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T08:13:45.754Z Has data issue: false hasContentIssue false
  • English
  • Français

The distribution of the pathogenic nematode Nematodirus battus in lambs is zero-inflated

Published online by Cambridge University Press:  14 July 2008

M. J. DENWOOD ,
M. J. STEAR ,
L. MATTHEWS ,
S. W. J. REID ,
N. TOFT  and
G. T. INNOCENT
M. J. DENWOOD*
Affiliation:
Comparative Epidemiology and Informatics, Institute for Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, UK
M. J. STEAR
Affiliation:
Veterinary Genes and Proteins Group, Institute for Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, UK
L. MATTHEWS
Affiliation:
Comparative Epidemiology and Informatics, Institute for Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, UK
S. W. J. REID
Affiliation:
Comparative Epidemiology and Informatics, Institute for Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, UK
N. TOFT
Affiliation:
Danish Meat Association, Vinkelvej 11, DK-8620 Kjellerup, Denmark
G. T. INNOCENT
Affiliation:
Comparative Epidemiology and Informatics, Institute for Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, UK
*
*Corresponding author: Comparative Epidemiology and Informatics, Institute for Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, UK. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Understanding the frequency distribution of parasites and parasite stages among hosts is essential for efficient experimental design and statistical analysis, and is also required for the development of sustainable methods of controlling infection. Nematodirus battus is one of the most important organisms that infect sheep but the distribution of parasites among hosts is unknown. An initial analysis indicated a high frequency of animals without N. battus and with zero egg counts, suggesting the possibility of a zero-inflated distribution. We developed a Bayesian analysis using Markov chain Monte Carlo methods to estimate the parameters of the zero-inflated negative binomial distribution. The analysis of 3000 simulated data sets indicated that this method out-performed the maximum likelihood procedure. Application of this technique to faecal egg counts from lambs in a commercial upland flock indicated that N. battus counts were indeed zero-inflated. Estimating the extent of zero-inflation is important for effective statistical analysis and for the accurate identification of genetically resistant animals.

Keywords

Nematodirus battusOvis ariesdistributionBayesian MCMCfaecal egg countZero-inflated negative binomial
Type
Original Articles
Information
Parasitology , Volume 135 , Issue 10 , September 2008 , pp. 1225 - 1235
Copyright
Copyright © 2008 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, R. M. and May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford Science Publications, Oxford, UK.Google Scholar
Armour, J. and Coop, R. L. (1991). Pathogenesis and control of gastrointestinal helminthiasis. In Diseases of Sheep (ed. Martin, W. B. and Aitken, I. D.), pp. 122–130. Blackwell Scientific Publications, Oxford, UK.Google Scholar
Armour, J., Jarrett, W. F. H. and Jennings, F. W. (1966). Experimental Ostertagia circumcincta infections in sheep: development and pathogenesis of a single infection. American Journal of Veterinary Research 27, 1267–1278.Google Scholar
Barger, I. A. (1985). The statistical distribution of trichostrongylid nematodes in grazing lambs. International Journal for Parasitology 15, 645–649.Google Scholar
Bishop, S. C., Jackson, F., Coop, R. L. and Stear, M. J. (2004). Genetic parameters for resistance to nematode infections in Texel lambs and their utility in breeding programmes. Animal Science 78, 185–194.Google Scholar
Bliss, C. I. and Fisher, R. A. (1953). Fitting the negative binomial distribution to biological data. Biometrics 9, 176–200.CrossRefGoogle Scholar
Dineen, J. K. (1963). Immunological aspects of parasitism. Nature, London 197, 268–269.CrossRefGoogle ScholarPubMed
Edwards, A. W. F. (1992). Likelihood Expanded Edition. The Johns Hopkins University Press, Baltimore, MD, USA.Google Scholar
Elliott, J. M. (1977). Some Methods for the Statistical Analysis of Samples of Benthic Invertebrates, 2nd Edn.Freshwater Biological Association, Ambleside, UK.Google Scholar
Gaba, S., Ginot, V. and Cabaret, J. (2005). Modelling macroparasite aggregation using a nematode-sheep system: the Weibull distribution as an alternative to the negative binomial distribution? Parasitology 131, 393–401.CrossRefGoogle Scholar
Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–511.Google Scholar
Hunter, G. C. and Quenouille, M. H. (1952). A statistical examination of the worm egg count sampling technique for sheep. Journal of Helminthology 26, 157–170.Google Scholar
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. Chapman and Hall, London.CrossRefGoogle Scholar
McEwan, J. C., Dodds, K. G., Greer, G. J., Bain, W. E., Duncan, S. J., Wheeler, R., Knowler, K. J., Reid, P. J., Green, R. S. and Douch, P. G. C. (1995). Genetic estimates for parasite resistance traits in sheep and their correlations with production traits. New Zealand Journal of Zoology 22, 177.Google Scholar
Morgan, E. R., Cavill, L., Curry, G. E., Wood, R. M. and Mitchell, E. S. E. (2005). Effects of aggregation and sample size on composite faecal egg counts in sheep. Veterinary Parasitology 128, 79–87.Google Scholar
Morris, C. A., Bisset, S. A., Vlassoff, A., West, C. J. and Wheeler, M. (2004). Genetic parameters for Nematodirus spp. egg counts in Romney lambs in New Zealand. Animal Science 79, 33–39.Google Scholar
Nødtvedt, A., Dohoo, I., Sanchez, J., Conboy, G., DesCÐteaux, L., Keefe, G., Leslie, K. and Campbell, J. (2002). The use of negative binomial modelling in a longitudinal study of gastrointestinal parasite burdens in Canadian dairy cows. Canadian Journal of Veterinary Science 66, 249–257.Google Scholar
Plummer, M., Best, N., Cowles, K. and Vines, K. (2006). coda: Output Analysis and Diagnostics for MCMC. R package version 0.10–7.Google Scholar
R Development Core Team (2007). R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.Google Scholar
Spiegelhalter, D. J., Thomas, A. and Best, N. G. (2003). WinBUGS Version 1.4 User Manual. MRC Biostatistics Unit.Google Scholar
Stear, M. J., Bairden, K., Bishop, S. C., Gettinby, G., McKellar, Q. A., Park, M., Strain, S. A. J. and Wallace, D. S. (1998). The processes influencing the distribution of parasitic nematodes among naturally infected lambs. Parasitology 117, 165–171.CrossRefGoogle ScholarPubMed
Toft, N., Innocent, G. T., Gettinby, G. and Reid, S. W. J. (2007). Assessing the convergence of Markov chain Monte Carlo methods: an example from evaluation of diagnostic tests in absence of a gold standard. Preventive Veterinary Medicine 79, 244–256.CrossRefGoogle ScholarPubMed
Torgerson, P. R., Schnyder, M. and Hertzberg, H. (2005). Detection of anthelmintic resistance: a comparison of mathematical techniques. Veterinary Parasitology 128, 291–298.CrossRefGoogle ScholarPubMed
van de Ven, R. (1993). Estimating the shape parameter of the negative binomial distribution. Journal of Statistical Computation and Stimulation 46, 111–123.Google Scholar
van de Ven, R. and Weber, N. (1999). On the asymptotic behaviour of estimators of the shape parameter of the negative binomial distribution. ISI 99 52nd Session. Bulletin of the International Statistical Institute, pp. 419–420. Edita Ltd, Helsinki, Finland.Google Scholar