Gastrointestinal nematode (GIN) 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. GIN infections can result in reduced feed intake and redirection of nutritional resources to combat infection causing decreased weight gain and clinical gastro-enteritis. Beyond the obvious negative impact on animal health and welfare, this threatens the economic viability of livestock production with estimated economic costs in sheep alone exceeding €357 million across Europe. Further, infection has also been shown to increase the greenhouse gas emissions of affected sheep, hampering progress towards ambitious net zero targets. Anthelmintics remain widely used to control GIN populations in livestock; however, parasite populations are increasingly developing resistance to these treatments and anthelmintics themselves have negative environmental impacts.  As such, we must seek novel methods by which the use of anthelmintics can be limited whilst the burden of GIN infection on livestock populations is reduced.

BioSS researchers Lee Benson, Giles Innocent and David Ewing recently worked with colleagues at the University of Liverpool, Queen’s University Belfast, Scotland’s Rural College (SRUC) and the Moredun Research Institute (MRI) to lead the development of GI-NemaTracker, a systems-level mathematical model of the full host-parasite-environment system governing GIN transmission on a sheep farm. By developing an individual-based modelling approach, they were able to test interventions that target treatment at specific animals, thus reducing the need for whole flock treatments and slowing the development of anthelmintic resistance. The model was validated against experimental and field data generated by collaborators at SRUC and MRI, and 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.

Future extensions and refinements of the model could see it used to directly predict how different farm management practices might influence the development of anthelmintic resistance over time and to help develop best practice guidelines that are tailored to different environmental conditions across the country.

This work is published in the International Journal of Parasitology. 

Acknowledgements

All authors were funded under BBSRC project BB/W020505/1 and researchers at BioSS, SRUC and MRI were supported by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS).