Evolutionary and epidemiological considerations for anthelmintic treatments in ruminant livestock.

Anthelmintic drug resistance is a concern for the sustained control of gastrointestinal nematodes (GINs) in ruminant livestock globally. Evolutionary-epidemiological modelling, which considers both parasite dynamics and resistance dynamics in response to interventions, can be useful in determining which anthelmintic resistance management (ARM) strategies may be effective without compromising parasite control. We address two key questions in ARM. First, how to improve the measurement of AR in populations. Second, identifying effective ARM strategies to slow the spread of AR while maintaining effective parasite control. We developed a simulation framework which tracks the weather-dependent epidemiology of GINs and AR evolution, providing a highly flexible methodology to evaluate multiple ARM strategy options in a single modelling framework, allowing for novel insights due to direct comparisons between strategies. Simulations to refine our understanding of anthelmintic resistance management evaluated the impact of key areas of uncertainty, including transmission intensity, resistance intensity, resistance frequency, drug decay and linking faecal egg count reduction tests (FECRT) to resistance allele frequency. Large-scale simulations present a methodologically thorough evaluation of how treatment choices simultaneously impact epidemiological and evolutionary outcomes. Phenotypic classifications of resistance status using FECRT failed to capture fine scale changes in resistance allele frequency. The pharmacokinetics of drug decay strongly influenced ARM outcomes, and trade-offs between ARM and effective parasite control depends on genetic factors underpinning resistance. Combination therapies appear to be the most effective resistance management strategy evaluated. Our findings suggest practical implementations to manage anthelmintic resistance must simultaneously consider parasite transmission, pharmacology and parasite genetics to be robust and sustainable. We provide a rigorous simulation framework to enable such discussions allowing for a refinement into our understanding of parasite control in the presence of resistance evolution.