Accurate assessment of the health status of individual animals is a key step in timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect levels of anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals over the Haemonchus season. Here, we show that accelerometers can measure individual activity in extensively grazing small ruminants subject to natural Haemonchus contortus worm infection in southern Africa over long time-scales, and when combined with machine learning, can predict the smallest pre-clinical increases in FAMACHA score as well as those individuals that respond to treatment, all with high precision (>95%). We demonstrate that these classifiers remain robust over time, and remarkably, generalise without retraining across goats and sheep in different regions and types of farming enterprise. Interpretation of the trained classifiers reveal that as the effect of haemonchosis increases, both sheep and goats exhibit a similar reduction in the fine-grained variation of their activity levels. Our study thus reveals common behavioural patterns across small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and hence diminish pressures on anthelmintic resistance under conditions of both commercial and resource-poor communal farming.