Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T16:11:59.906Z Has data issue: false hasContentIssue false

Parasite aggregations in host populations using a reformulated negative binomial model

Published online by Cambridge University Press:  12 April 2024

P. Pal*
Affiliation:
School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK
J.W. Lewis
Affiliation:
School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK
*
*Fax: +44 1784 434348 Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The negative binomial distribution model is reformulated and used to demarcate a host population at a specific level of infection by defining an attribute spanning a range of parasite aggregations. The upper limit of the range specifies the boundary for the classification of the host population and provides a technique to determine the cumulative probability at any level of parasite infection to a high degree of accuracy. This approach also leads to the evaluation of the k parameter, i.e. an inverse measure of dispersion of parasite aggregation, for each fraction of the host population with a discrete level of infection. The basic mathematical premise of the negative binomial function is unaltered in developing this reformulation which was applied to data on the distribution of the trichostrongylid nematode Heligmosomoides polygyrus in populations of the field mouse, Apodemus sylvaticus.

Type
Review Article
Copyright
Copyright © Cambridge University Press 2004

References

Anderson, R.M. & May, R.M. (1978) Regulation and stability of host–parasite population interactions. I. Regulatory processes. Journal of Animal Ecology 47, 219247.CrossRefGoogle Scholar
Behnke, J.M., Lewis, J.W., Mohd Zain, S.N. & Gilbert, F.S. (1999) Helminth infections in Apodemus sylvaticus in southern England: interactive effects of host age, sex, and years on the prevalence and abundance of infections. Journal of Helminthology 73, 3144.CrossRefGoogle ScholarPubMed
Bliss, C.A. & Fisher, R.A. (1953) Fitting the negative binomial to biological data and a note on the efficient fitting of the negative binomial. Biometrics 9, 176200.CrossRefGoogle Scholar
Crofton, H.D. (1971) A model of host–parasite relationships. Parasitology 63, 343364.CrossRefGoogle Scholar
Gregory, R.D., Woolhouse, M.E.J. (1993) Quantification of parasite aggregation: a simulation study. Acta Tropica 54, 131139.CrossRefGoogle ScholarPubMed
Lewis, J.W. (1987) Helminth parasites of British rodents and insectivores. Mammal Review 17, 8193.CrossRefGoogle Scholar
May, R.M. (1977) Dynamical aspects of host–parasite associations: Crofton's model re-visited. Parasitology 75, 259276.CrossRefGoogle Scholar
Pielou, E.C. (1977) Mathematical ecology. New York, J. Wiley.Google Scholar
Smith, G., Basanez, M.-G., Dietz, K., Gemmell, M.A., Grenfell, B.T., Gulland, F.M.D., Hudson, P.J., Kennedy, C.R., Lloyd, S., Medley, G., Nassel, I., Randolph, S.E., Roberts, M.G., Shaw, D.J. & Woolhouse, M.E. (1991) Macroscopic group report: problems in modelling the dynamics of macroscopic systems. pp. 209229 in Grenfell, B.T. & Dobson, A.P. (Eds) Ecology of infectious diseases in natural populations. Cambridge, Cambridge University Press.Google Scholar