GI-NemaTracker – A farm system-level mathematical model to predict the consequences of gastrointestinal parasite control strategies in sheep

Gastro-intestinal nematode infections are considered one of the major endemic diseases of sheep on the grounds of animal health and economic burden, both in the British Isles and globally. Parasites are increasingly developing resistance to commonly used anthelmintic treatments meaning that alternative control strategies that reduce or replace the use of anthelmintics are required. We present GI-NemaTracker, a systems-level mathematical model of the full host-parasite-environment system governing gastro-intestinal nematode transmission on a sheep farm. The model is based on a series of time-varying delay-differential equations that explicitly capture environmentally-driven time delays in nematode development. By taking a farm systems-level approach we represent both in–host and environmentally-driven free-living parasite dynamics and their interaction with a population of individually modelled lambs with diverse trait parameters assigned at birth. Thus we capture seasonally varying rates of parasite transmission and consequently variable weight gain of individual lambs throughout the season. The model is parameterised for Teladorsagia circumcincta, although the framework described could be applied to a range of nematode parasite species. We validate the model against experimental and field data and apply it to study the efficacy of four different anthelmintic treatment regimes (neo-suppresive treatment, strategic prophylactic treatment, treatment based on faecal egg counts and a regime which leaves $10\%$ of the animals untreated) on lamb weight gain and pasture contamination. The model predicts that similar body weights at a flock level can be achieved while reducing the number of treatments administered, thus supporting a health plan that reduces anthelmintic treatments. As the model is capable of combining parasitic and free-living stages of the parasite with host performance, it is well suited to predict complex system responses under non-stationary conditions. The implications of the model and its potential as a tool in the development of sustainable control strategies in sheep are discussed.