The study, published in the journal Parasites & Vectors, exploits a regulatory quirk that most surveillance systems can only dream of. Unlike passive reporting schemes, which depend on farmers choosing to notify disease, the Imizol notification requirement captures treatment events systematically, because compliance is mandated whenever the drug is used. Between 2016 and 2024, these notifications provided a proxy dataset for clinically recognised bovine babesiosis at a resolution fine enough to map disease presence onto a grid of two-kilometre squares covering Great Britain. The disease itself is caused by protozoan parasites of the genus Babesia, transmitted by ticks, with the sheep tick Ixodes ricinus serving as the principal vector in the British Isles. Infected cattle can develop fever, anaemia, red urine and, in severe cases, death, making early detection and treatment critical for herd health and farm economics.

To transform the notification records into a predictive map, the team turned to machine learning, specifically boosted regression trees, a method that excels at capturing complex, nonlinear relationships between environmental variables and disease occurrence. The predictor variables fell into four broad categories. Seasonal climate metrics characterised temperature and moisture conditions across the year. Vegetation greenness, derived from satellite observations, served as a proxy for habitat quality and tick-friendly landscapes. Livestock densities captured the availability of hosts on which ticks could feed. Finally, the researchers included predicted habitat suitability for three species of deer, red, roe and fallow, which act as key reproductive hosts for adult Ixodes ricinus ticks and are known to shape tick population dynamics across British landscapes.

The modelling strategy was deliberately rigorous. Models were trained on notification data from southwest England, a recognised hotspot for the disease, using records from 2016 to 2022. They were then temporally validated against data from the same region for 2023 and 2024, testing whether the models could anticipate disease patterns in years they had never seen. Crucially, the models were also evaluated externally against independent notification data from outside the training region, a demanding test of whether the relationships learned in one part of Britain could transfer to environments elsewhere on the island.

The results were striking. In internal cross-validation, the models achieved a mean area under the receiver operating characteristic curve, or AUC, of 0.81, with a mean correlation of 0.55, indicating strong discrimination between areas with and without notified cases. Temporal validation yielded a continuous Boyce Index of 0.81, confirming that the models reliably ranked habitat suitability in unseen years. External validation outside southwest England produced a Boyce Index of 0.58, suggesting moderate transferability, a respectable figure for a disease model extrapolated beyond its training domain. For a condition that has historically lacked any systematic national mapping, these performance metrics represent a substantial advance in the epidemiology of bovine babesiosis.

Perhaps the most revealing findings concerned which variables actually drove the predicted patterns. Climatic predictors accounted for the greatest relative influence by a wide margin, with autumn cooling alone contributing a mean relative influence of 37.3 percent and atmospheric moisture adding a further 8.5 percent. This dominance of autumnal temperature decline aligns closely with the ecology of Ixodes ricinus, whose activity depends on specific thermal and humidity windows. Ticks in Britain typically show peaks of questing activity in spring and autumn, and the timing and intensity of autumn cooling appears to constrain how long ticks remain active late in the season, shaping the window during which cattle can be exposed to infected bites.

Among the non-climatic predictors, habitat suitability for roe deer and sheep density emerged as the most influential factors, each contributing moderate explanatory power, while vegetation greenness exerted a smaller effect. The prominence of roe deer is ecologically coherent: deer of this species are widespread across woodland and fragmented rural landscapes in Britain and serve as efficient hosts for adult ticks, amplifying tick populations in areas where they thrive. Sheep density, meanwhile, may reflect both the presence of additional hosts and the movement of livestock between farms, which can transport ticks and, in some contexts, the parasites themselves. Notably, the proportional land-cover variables that are staples of many landscape epidemiology studies showed consistently low individual influence, suggesting that the composition of habitat types matters less for this disease than the climatic and host-related conditions that govern tick survival and abundance.

Beyond its ecological insights, the study carries immediate practical value for British agriculture. The risk maps generated by the models can help anticipate areas of elevated babesiosis risk and guide disease surveillance at regional scales. Of particular interest are the locations where environmental conditions appear suitable for disease occurrence but treated cases have not yet been reported. Such mismatches between predicted suitability and observed notifications may indicate genuine gaps in surveillance, under-recognition of disease by veterinarians, or areas where the disease is emerging as conditions change. Identifying these frontier zones allows veterinary authorities and farmers to target awareness campaigns, diagnostic efforts and tick control measures before outbreaks become established.

The research also demonstrates a methodological lesson with implications far beyond a single disease. Mandatory veterinary medicines notification data, the authors show, can serve as a credible proxy for disease occurrence at national scale, opening a surveillance channel that exists independently of voluntary reporting. Many countries maintain compulsory reporting requirements for the use of specific veterinary drugs, and the British experience suggests these records can be mined for spatial epidemiology at little additional cost. As climate change alters the seasonal rhythms of temperature and moisture across Europe, the activity patterns of Ixodes ricinus and the diseases it carries are expected to shift, making robust, repeatable mapping systems increasingly valuable.

For British cattle farmers, the message is that the geography of babesiosis risk is now legible in unprecedented detail, and that the levers shaping that geography are largely environmental: the coolness of autumn, the dampness of the air, the abundance of roe deer in the surrounding woods and the density of sheep grazing nearby fields. The work, funded through a University of Liverpool PhD studentship in partnership with the Animal and Plant Health Agency and supported by the BBSRC-DEFRA OPTICK project, exemplifies how collaboration between academic modellers and government surveillance units can convert routine regulatory data into actionable ecological intelligence. As warming winters and shifting rainfall patterns continue to redraw the map of tick-borne disease across the British Isles, studies of this kind provide the baseline against which future change will be measured, and the tools with which farmers and veterinarians can stay one step ahead of a parasite that thrives in the margins of the season.

Subject of Research: Spatial modelling of bovine babesiosis risk in Great Britain using mandatory imidocarb treatment notifications and machine learning

Article Title: Climate, host and landscape determinants of bovine babesiosis in Great Britain: analysis of Imidocarb treatment notifications, 2016–2024

Article References: Shanks, S., Duncan, J., Johnson, N., Millins, C., Hassall, R., & Purse, B. V. (2026). Climate, host and landscape determinants of bovine babesiosis in Great Britain: analysis of Imidocarb treatment notifications, 2016–2024. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07665-x

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07665-x

Keywords: bovine babesiosis, Ixodes ricinus, tick-borne disease, imidocarb dipropionate, machine learning, boosted regression trees, climate determinants, roe deer, sheep density, Great Britain, veterinary surveillance, risk mapping