Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle.

💡 Research Summary

Bovine tuberculosis (TB) remains a persistent threat to cattle industries, particularly in the United Kingdom and Ireland where wildlife reservoirs—most notably European badgers—complicate eradication efforts. Traditional epidemiological models have largely focused on cattle alone, neglecting the complex inter‑species dynamics that can sustain infection cycles. In this study, the authors develop a novel coupled modelling framework that integrates two spatially explicit individual‑based models (IBMs): one for cattle herds and one for badger populations. Each model operates on a different grid resolution appropriate to the biology of the host species, yet they interact through defined contact zones where cattle and badgers may transmit Mycobacterium bovis.

The cattle IBM represents farms as nodes within a county‑scale network. It incorporates demographic processes (birth, death, movement), management practices (winter housing, which aggregates animals into high‑density indoor pens during the cold season), and disease processes (infection, progression, testing, culling). Winter housing is modelled as a seasonal increase in within‑farm contact rates, reflecting the heightened transmission risk observed in real‑world outbreaks. The badger IBM simulates sett colonies distributed across the landscape, with parameters for reproduction, natural mortality, and dispersal derived from field studies. Badger movement is limited to a defined neighbourhood, and infection can spread both within colonies and between neighbouring colonies.

Coupling is achieved by overlaying the two grids and assigning a probability of inter‑species contact at each intersecting cell. This probability depends on distance, local animal densities, and seasonal behaviour (e.g., increased outdoor grazing in summer). The authors therefore capture both direct transmission events (e.g., a badger entering a cattle pen) and indirect pathways (environmental contamination shared across overlapping habitats).

To explore the high‑dimensional parameter space, the authors employed Latin Hypercube Sampling (LHS), generating over 10,000 distinct parameter sets. Each simulation ran for 30 years with annual time steps, producing two primary outputs: the average number of infected cattle per year and the prevalence of infection in badgers. Sensitivity analysis was conducted using mixed‑effects regression models, treating the simulation runs as random effects while evaluating fixed effects such as:

1. Frequency of cattle TB testing (0–3 tests per year)
2. Presence or absence of winter housing, and its adoption rate relative to farm size
3. Intensity of badger culling (0–100 % of the local population)
4. Diagnostic test accuracy (sensitivity and specificity)
5. Baseline inter‑species contact rates and movement parameters

The mixed‑effects analysis revealed that testing frequency accounted for the largest proportion of variance in infected cattle numbers (≈ 38 %). Winter housing contributed a substantial second share (≈ 27 %), with its impact magnifying as farm size increased—large operations that house many animals together experienced markedly higher infection levels when winter housing was practiced. Badger culling explained only about 5 % of the variance, suggesting that removal of wildlife alone does not substantially disrupt the cattle infection cycle under the modeled conditions. Improvements in diagnostic accuracy contributed less than 3 % of the variance, indicating that increasing test sensitivity or specificity yields diminishing returns compared to more frequent testing or management changes.

Key epidemiological insights emerged from these results. First, increasing the number of cattle TB tests per year yields a non‑linear reduction in infection prevalence; moving from a single annual test to two or more tests can halve the average number of infected cattle. Second, winter housing creates high‑density contact networks that act as super‑spreading environments, especially in larger farms where the absolute number of animals aggregated is greater. Third, the modest effect of badger culling aligns with field observations that culling can be socially and ecologically disruptive without guaranteeing a proportional drop in cattle cases, possibly because of compensatory badger immigration or altered movement patterns. Finally, while diagnostic test performance is important for individual case detection, its population‑level impact is outweighed by the frequency of testing and housing practices.

From a policy perspective, the study suggests that resources should prioritize increasing testing frequency and re‑evaluating winter housing protocols rather than relying solely on wildlife culling. For large farms, strategies such as staggered housing, reduced pen density, or alternative seasonal management could mitigate the amplified risk identified in the model. The coupled IBM framework also provides a flexible platform for evaluating other multi‑host diseases (e.g., brucellosis, African swine fever) and for tailoring interventions to specific geographic contexts.

In conclusion, the authors present a robust, spatially explicit, dual‑species modelling approach that successfully reproduces observed cattle and badger population dynamics and captures the rising trend in bovine TB prevalence. By systematically sweeping a large parameter space and applying mixed‑effects sensitivity analysis, they identify testing frequency and winter housing as the dominant levers for disease control, with badger culling and test accuracy playing comparatively minor roles. This work advances our mechanistic understanding of TB transmission across species and offers actionable guidance for policymakers seeking cost‑effective, evidence‑based strategies to curb bovine tuberculosis.