Early prediction of declining health in small ruminants with accelerometers and machine learning

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 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. Here, we used accelerometers to measure the individual activity of extensively grazing small ruminants (sheep and goats) exposed to natural Haemonchus contortus worm infection in southern Africa over long time-scales (13+ months). When combined with machine learning, this activity data can predict poorer health (increases in FAMACHA score), as well as those individuals that respond to treatment, all with precision up to 80%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveal that poorer health mainly affects the night-time activity levels, in both sheep and goats. Our study thus reveals common behavioural patterns across two 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 diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming.