Predicting the current and future risk of ticks on livestock farms in Britain using random forest models

The most abundant tick species in northern Europe, Ixodes ricinus, transmits a range of pathogens that cause disease in livestock. As I. ricinus distribution is influenced by climate, tick-borne disease risk is expected to change in the future. The aims of this work were to build a spatial model to predict current and future risk of ticks on livestock farms across Britain. Variables relating both to tick hazard and livestock exposure were included, to capture a niche which may be missed by broader scale models. A random forest machine learning model was used due to its ability to cope with correlated variables and interactions. Data on tick presence and absence on sheep and cattle farms was obtained from a retrospective questionnaire survey of 926 farmers. The ROC of the final model was 0.80. The model outputs matched observed patterns of tick distribution, with areas of highest tick risk in southwest and northwest England, Wales, and west Scotland. Overall, the probability of tick presence on livestock farms was predicted to increase by 5-7 % across Britain under future climate scenarios. The predicted increase is greater at higher altitudes and latitudes, further increasing the risk of tick-borne disease on farms in these areas.

Automated summary

This study built a spatial model to predict the risk of ticks on livestock farms in Britain, taking into account variables related to tick hazard and livestock exposure. Using a random forest machine learning model, the predicted results matched observed patterns of tick distribution, with the highest risk areas being in southwest and northwest England, Wales, and west Scotland. The probability of tick presence on livestock farms is predicted to increase by 5-7% across Britain under future climate scenarios, with a greater increase at higher altitudes and latitudes, further increasing the risk of tick-borne diseases.