Anthelmintic resistance revisited: Under-dosing, chemoprophylactic strategies, and mating probabilities

Gary Smith, Bryan T. Grenfell, Valerie Isham, Stephen Cornell

Research output: Contribution to journalArticlepeer-review

97 Scopus citations

Abstract

Deterministic and stochastic models are used to examine the evolution of anthelmintic resistance among trichostrongylid parasites of domestic ruminants. We find that the relative selection pressures exerted by chemoprophylactic (preventive) control strategies, chemotherapeutic (salvage) control strategies, and regimens involving under-dosing are critically dependent on a variety of host and parasite parameters (particularly host immunity and grazing behaviour, parasite fecundity, and the survival of the free-living stages on the pasture). Chemoprophylactic strategies are not necessarily more likely to exert a stronger selection pressure than chemotherapeutic strategies. Similarly, as one reduces dosage levels, there is a range of dose levels where under-dosing promotes resistance and a range of dose levels where under-dosing impedes resistance. The most dangerous dose is either that necessary to kill all the susceptible homozygotes, or that necessary to kill all the susceptible homozygotes and all the heterozygotes. Which one prevails depends upon model parameters. The stochastic formulation indicates that spatial heterogeneity in transmission may be a significant force in promoting the spread of resistant genotypes-at least when infection is at low levels. Copyright (C) 1998 Australian Society for Parasitology. Published by Elsevier Science Ltd.

Original languageEnglish (US)
Pages (from-to)77-91
Number of pages15
JournalInternational Journal for Parasitology
Volume29
Issue number1
DOIs
StatePublished - Jan 1999
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Infectious Diseases
  • Parasitology

Keywords

  • Anthelmintic resistance
  • Mathematical model
  • Parasite distributions
  • Spatial heterogeneity in transmission
  • Stochastic model
  • Under-dosing

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