AI disease prediction for cattle herds
Imagine a 5000-head cattle complex, losing 2–3 animals daily to respiratory infections, with vaccination applied post-factum. Instead of reactive measures — quarantine and culling — we deploy an ML pipeline that analyzes data from activity sensors, weather reports, and veterinary records in real time. The system warns about an outbreak likelihood 2–14 days in advance, allowing targeted isolation of risk groups and feed adjustments. The 7-day prediction accuracy reaches 82% — confirmed in a pilot on 5000 head of cattle. Treatment and loss savings reach 30% — for a 3000-head farm, that's about 1.5 million rubles per year. Contact us for a consultation to evaluate the potential for your herd.
How ML models predict outbreaks?
Classical epidemiology uses the SIR model (SIR model, Wikipedia). But in a closed herd with controlled movements, this is insufficient. We combine several approaches: time series, spatial statistics, and survival analysis. Survival analysis outperforms the SIR model by 0.04–0.06 in F1-score when predicting chronic diseases such as mastitis. The spatial model is 1.3 times more accurate than SIR for BRDC prediction (F1=0.81 vs 0.75). Our AI disease prediction system uses these methods for early outbreak warning, integrates with VetIS, and delivers accuracy up to 85%.
Extended parameter SIR model:
from scipy.integrate import odeint def sir_model(y, t, beta, gamma, N): """ S = susceptible, I = infected, R = recovered dS/dt = -beta * S * I / N dI/dt = beta * S * I / N - gamma * I dR/dt = gamma * I """ S, I, R = y dS = -beta * S * I / N dI = beta * S * I / N - gamma * I dR = gamma * I return [dS, dI, dR] # Parameters for BVD beta_bvd = 0.3 gamma_bvd = 0.1 N_herd = 200 solution = odeint(sir_model, y0=[N_herd-1, 1, 0], t=np.linspace(0, 90, 90), args=(beta_bvd, gamma_bvd, N_herd)) Real-world cases: BRDC, mastitis, foot-and-mouth disease
From our practice: for a large dairy complex with 3000 head, we implemented mastitis prediction. Main predictors — somatic cell count in bulk milk, new infection rate, and teat hygiene. A survival model with a random forest ensemble gave F1=0.79 on a 30-day horizon. For respiratory diseases (BRDC) in beef cattle, key factors are transport stress and herd mixing; the model warns 5–7 days before an outbreak with 81% accuracy. Foot-and-mouth disease (FMD) is a special case: an ML signal about a sudden drop in mobility of many animals triggers a notification to Rosselkhoznadzor via VetIS. Get demo access to the system on your data to see how it works with real scenarios.
Example of mastitis risk assessment:
def herd_mastitis_risk_score(herd_data): return { 'bulk_milk_scc': herd_data['bulk_tank_scc'], 'new_infection_rate': herd_data['new_cases_monthly'] / herd_data['herd_size'], 'cure_rate': herd_data['spontaneous_cures_pct'], 'chronic_cow_pct': herd_data['recurring_cases_pct'], 'hygiene_score': herd_data['teat_condition_score'] } Why is herd immunity important for prediction?
R₀ (basic reproduction number) determines the vaccination threshold to suppress an outbreak. For BVD, R₀ ≈ 2–4, minimum vaccination coverage is 60–70% of the herd. Our optimizer schedules vaccinations to keep R_effective < 1. Savings on vaccines and treatment can reach 30%. Contact us to calculate the optimal vaccination strategy for your herd.
MLOps pipeline and model validation
Deploying the prediction system includes continuous validation: monitoring data drift and retraining models quarterly. We use MLflow for versioning and Kubeflow for pipeline orchestration. This ensures stable accuracy above 78% even with changing seasons or herd composition. For quality control, we also implement A/B testing of new models on historical data.
Comparison of prediction methods
| Method | Data | Forecast horizon | Accuracy (F1) |
|---|---|---|---|
| SIR + regressors | Weekly cases, weather | 2–4 weeks | 0.75 |
| Spatial model | Geolocation, contacts | 7–14 days | 0.81 |
| Random Survival Forest | History, physiology | 30–90 days | 0.79 |
| Disease | Key data | Optimal model |
|---|---|---|
| Mastitis | Somatic cells, hygiene | Survival forest (F1=0.79) |
| BRDC | Transport, weather | Spatial model (F1=0.81) |
| Foot-and-mouth | Mobility, contacts | SIR + spatial effects |
What does developing a prediction system include?
We deliver a turnkey ML pipeline:
- Data collection and cleaning (sensors, veterinary logs, weather APIs)
- Model training for the specific disease (BVD, BRDC, mastitis)
- Dashboard with alert levels (green/yellow/red)
- Integration with FSIS VetIS for notifiable diseases
- Staff training and documentation
The company has 10+ years of experience in AI for agriculture and has completed 20+ projects in disease prediction. We guarantee accuracy not lower than 78% on a test set. We will assess your project in 2 days — just contact us for a consultation. Get demo access to the system on your data.