Modelling anthelmintic resistance by extending eggCounts package to allow individual efficacy

Area of Science:

• Veterinary Parasitology
• Statistical Modeling
• Animal Health

Background:

• Anthelmintic treatments show variable efficacy in individual animals, even with susceptible parasite populations.
• Accurate estimation of drug efficacy and resistance is crucial for livestock health management.

Purpose of the Study:

• To extend the R package eggCounts with a Bayesian hierarchical model incorporating individual animal efficacy.
• To compare the performance of the proposed model against other methods for estimating faecal egg count reduction.

Main Methods:

• Development of an extended Bayesian hierarchical model within the R package eggCounts.
• Conducting a simulation study to compare five different estimation methods for faecal egg count reduction.
• Application of the model to a real-world dataset of sheep and cattle faecal egg counts from the USA.

Main Results:

• The individual efficacy model demonstrated robust inference across different data simulation procedures.
• Low root mean squared error was observed for the reduction estimate, with appropriate uncertainty quantification.
• Strong anthelmintic resistance was detected in the USA dataset, confirming the model's practical utility.

Conclusions:

• The proposed Bayesian model effectively accounts for individual animal efficacy, improving accuracy in estimating anthelmintic effectiveness.
• The updated open-source statistical tools provide a valuable resource for detecting and managing anthelmintic resistance in livestock.
• This approach enhances the reliability of drug efficacy assessments and supports informed treatment decisions in veterinary medicine.