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Modelling anthelmintic resistance by extending eggCounts package to allow individual efficacy

Wang, Craig; Torgerson, Paul R; Kaplan, Ray M; George, Melissa M; Furrer, Reinhard (2018). Modelling anthelmintic resistance by extending eggCounts package to allow individual efficacy. International Journal for Parasitology: Drugs and Drug Resistance, 8(3):386-393.

Abstract

The same anthelmintic treatment can have variable efficacy on individual animals even if the parasite population is homogenously susceptible. An extension of the R package eggCounts is proposed to take individual efficacy into account using a Bayesian hierarchical model. A simulation study is conducted to compare the performance of five different methods on estimating faecal egg count reduction and its uncertainty interval. Simulation results showed the individual efficacy model offered robust inference to two different data simulation procedures with low root mean squared error on the reduction estimate and appropriate uncertainty estimates. Different methods were used to evaluate the anthelmintic resistance in a dataset from USA with sheep and cattle faecal egg counts, where a strong anthelmintic resistance was detected. Open-source statistical tools were updated to include the proposed model.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Department of Astrophysics
05 Vetsuisse Faculty > Veterinärwissenschaftliches Institut > Chair in Veterinary Epidemiology
Dewey Decimal Classification:510 Mathematics
Scopus Subject Areas:Life Sciences > Parasitology
Health Sciences > Infectious Diseases
Health Sciences > Pharmacology (medical)
Uncontrolled Keywords:Pharmacology (medical), Pharmacology, Parasitology, Infectious Diseases
Language:English
Date:1 December 2018
Deposited On:15 Nov 2018 14:04
Last Modified:19 Mar 2025 02:54
Publisher:Elsevier
ISSN:2211-3207
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.ijpddr.2018.07.003
Project Information:
  • Funder: SNSF
  • Grant ID: 200021_175529
  • Project Title: Disentangling evidence from huge multivariate space-time data from the earth sciences
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  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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