ajay2k3/livestock-health-disease-ssa-synthetic

Dataset Card: Livestock Health & Disease Surveillance (Synthetic Data)

Dataset Summary

This synthetic dataset represents 1,000,000 African smallholder households with livestock systems, capturing livestock health, disease surveillance, veterinary access, and herd management practices across Sub-Saharan Africa. It combines baseline farm characteristics (Dataset 1) with 15 livestock-specific variables to create a comprehensive picture of livestock production systems and animal health challenges.

Key Features:

• 1M households across 5 agro-ecological zones
• 27 variables (12 base farm + 15 livestock health)
• African-specific livestock systems and diseases
• Literature-grounded distributions (50+ peer-reviewed sources)
• Conditional dependencies modeling real-world relationships
• Realistic missing data patterns

Variables

Base Farm Characteristics (Dataset 1 - 12 variables)

1. agro_ecological_zone: Arid, semi-arid, sub-humid, humid, highland
2. region_type: Urban, peri-urban, rural accessible, rural remote
3. farm_size_ha: Farm size in hectares
4. soil_quality_index: Soil quality (0-100 scale)
5. rainfall_mm_annual: Annual rainfall (mm)
6. household_size: Number of household members
7. market_distance_km: Distance to nearest market
8. livestock_tlu: Tropical Livestock Units owned
9. extension_access: Access to agricultural extension (yes/no)
10. fertilizer_use_kg_ha: Fertilizer application rate
11. rainfall_mm_season: Seasonal rainfall (mm)
12. maize_yield_kg_ha: Maize yield (kg/ha)

Livestock Health & Production (NEW - 15 variables)

Herd Composition

13. herd_size_cattle: Number of cattle owned (0-50+)
14. herd_size_small_ruminants: Sheep and goats owned (0-100+)
15. poultry_count: Chickens, ducks, etc. (0-200+)

Veterinary Services & Access

16. vet_distance_km: Distance to nearest veterinary service (1-200 km)
17. vaccination_coverage_pct: % of herd vaccinated (0-100%)
18. vet_visit_annual: Had veterinary visit in past year (yes/no)

Disease & Health

19. disease_incidence_annual: Reported disease in past year (yes/no)
20. disease_type: Type of disease (FMD, ECF, CBPP, trypanosomiasis, PPR, Newcastle, respiratory, diarrhea, other)
21. mortality_rate_annual_pct: Annual livestock mortality rate (%)
22. pasture_quality_index: Pasture/rangeland quality (0-100 scale)

Management Systems

23. grazing_system: Type of grazing (communal, private, mixed, zero-grazing)
24. water_source_reliability: Water availability (year-round, seasonal, unreliable)
25. treatment_access: Type of treatment accessed (none, traditional, veterinary, both)
26. feed_supplementation: Provides supplementary feed (yes/no)
27. livestock_dependency_index: Household dependence on livestock (0-100 scale)

Dataset Statistics

Livestock Ownership

• 43.4% of households own cattle
• 62.9% own small ruminants (sheep/goats)
• 67.5% keep poultry
• Mean cattle herd size: ~5 animals (among owners)
• Mean small ruminant herd: ~12 animals (among owners)
• Mean poultry flock: ~8 birds (among keepers)

Disease Burden

• 32.7% reported disease incidence in past year
• Most common diseases:
  • Newcastle disease (poultry): 20%
  • FMD (Foot & Mouth): 18%
  • PPR (Peste des Petits Ruminants): 15%
  • ECF (East Coast Fever): 12%
  • Trypanosomiasis: 10%

Veterinary Access

• 40.3% had veterinary contact in past year
• Mean distance to vet services: 58.9 km
• 20% vaccination coverage (median)
• Treatment types:
  • 35% no treatment
  • 45% traditional remedies only
  • 15% veterinary treatment
  • 5% both traditional and veterinary

Management Practices

• 50% use communal grazing systems
• 25% private grazing
• 20% mixed systems
• 5% zero-grazing (intensive)
• 30% provide feed supplementation
• 40% have year-round water access
• 35% seasonal water only
• 25% unreliable water

Uses

Permitted Uses

• Livestock policy analysis: Model impacts of disease control programs
• Veterinary service planning: Optimize clinic placement and mobile vet routes
• Disease surveillance system design: Test outbreak detection algorithms
• Animal health research: Train ML models for disease prediction
• One Health initiatives: Link livestock-human health systems
• Extension service planning: Target interventions by livestock system type
• Educational purposes: Teaching livestock epidemiology and policy
• Climate adaptation: Model livestock system resilience
• Value chain analysis: Link livestock production to markets
• Research method development: Test statistical techniques

Prohibited Uses

• Not for replacement of real data collection: Cannot substitute for actual field surveys
• Not for country-specific policy: Too generalized for single-country decisions
• Not for real-time disease outbreak response: Not actual surveillance data
• Not for individual farmer targeting: Synthetic households are not real
• Not for precise cost-benefit analysis: Use for methodological prototypes only

Dataset Creation

Why This Dataset Exists

Real livestock health data in Sub-Saharan Africa faces critical gaps:

1. Surveillance gaps: Most countries lack systematic disease surveillance
2. Underreporting: Livestock diseases often go unreported (especially in remote areas)
3. Fragmented data: Information scattered across vet clinics, ministries, NGOs
4. Access restrictions: Sensitive disease data rarely shared publicly
5. High collection costs: Surveys expensive and logistically challenging
6. Privacy concerns: Household-level data cannot be openly published

This synthetic dataset enables:

• Algorithm development without waiting for data access
• Training of researchers and students
• International collaboration without data sharing barriers
• Rapid prototyping of livestock information systems
• Evidence generation for funding proposals

Creation Methodology

Rigorous 4-stage process following synthetic data best practices:

Stage 1: Literature Review (50+ sources)

• Systematic review of livestock systems in SSA
• Disease prevalence studies (FMD, ECF, trypanosomiasis, PPR, Newcastle)
• Veterinary service coverage assessments
• Management practice surveys
• Mortality and productivity benchmarks

Stage 2: Parameter Specification (15 files, 60-150 lines each)

• Conditional probability distributions by zone, region, herd size
• Functional relationships (e.g., vet distance → vaccination rates)
• Species-specific disease patterns
• Management system typologies
• Full provenance tracking

Stage 3: Conditional Data Generation

• Base variables from Dataset 1 (smallholder farms)
• Sequential generation respecting dependencies
• Zero-inflated distributions for herd sizes
• Categorical conditioning for disease types
• Realistic missing data (MCAR: 1-10%)

Stage 4: Validation

• Cross-variable consistency checks
• Literature benchmark comparisons
• Logical constraint verification
• Distribution shape validation

Limitations and Biases

Known Limitations

1. Oversimplified disease dynamics: Real disease spread is more complex than modeled
2. Static snapshot: No temporal dynamics (outbreaks, seasonality within year)
3. No spatial clustering: Real diseases show geographic clustering not captured
4. Coarse zones: 5 AEZ categories don't capture local variation
5. Missing variables: No breed info, no herd demographics, no animal-level data
6. Treatment outcomes: No data on treatment success/failure
7. No cost data: Disease impacts measured only in mortality, not economics
8. Simplified grazing: Complex pastoral mobility patterns simplified
9. Binary disease incidence: Real incidence is more granular (multiple episodes)

Potential Biases

1. Literature bias: Sources mostly from East Africa (Kenya, Tanzania, Ethiopia)
2. Veterinary access: May overestimate coverage in very remote pastoral areas
3. Disease reporting: Literature likely underrepresents mild/unreported diseases
4. Poultry systems: Village chickens well-represented, commercial systems underrepresented
5. Traditional knowledge: Traditional treatment effectiveness may be under-captured
6. Gender: No gender disaggregation of livestock ownership/management
7. Wealth gradient: Livestock wealth distribution may be too uniform
8. Conflict zones: Data may not reflect pastoralist areas affected by conflict

What This Dataset Is NOT

• ❌ Not real surveillance data: Do not use for actual disease outbreak decisions
• ❌ Not predictive: Cannot predict real disease occurrence
• ❌ Not country-specific: Generalized SSA patterns, not any single country
• ❌ Not longitudinal: Single time point, no panel structure
• ❌ Not spatially explicit: No GPS coordinates, no spatial autocorrelation

Technical Specifications

File Formats

• CSV: livestock_data.csv (315 MB, 1M rows)
• Parquet: livestock_data.parquet (111 MB, compressed)
• Metadata: metadata.json (generation parameters, sources)
• Data Dictionary: data_dictionary.csv (variable descriptions)

Missing Data

Realistic missing data rates by variable:

• Herd sizes: 2%
• Vet distance: 4%
• Vaccination coverage: 5%
• Disease incidence: 3%
• Pasture quality: 6%
• Mortality rate: 3%
• Disease type: 10% (conditional on disease occurrence)
• Management variables: 3-4%

Data Quality Indicators

• ✅ All constraints validated (no impossible values)
• ✅ Conditional dependencies respected
• ✅ Literature benchmarks matched (±10%)
• ✅ Cross-variable correlations logical
• ✅ Missing data patterns realistic

Ethical Considerations

Privacy

• No real households: All data fully synthetic, cannot identify real people/places
• No GPS coordinates: No geographic identifiers that could reveal locations
• Aggregated patterns only: Individual records are fictional

Representation

• Pan-African focus: Captures diversity across SSA, not dominated by single region
• Pastoral systems included: Arid/semi-arid zones well-represented
• Smallholder-centric: Large commercial farms not included
• Traditional knowledge: Ethnoveterinary practices acknowledged

Responsible Use

Users should:

• ✅ Clearly label outputs as based on synthetic data
• ✅ Validate methods on real data before deployment
• ✅ Not overstate generalizability of findings
• ✅ Cite real data sources when transitioning to applications
• ✅ Engage local stakeholders when designing interventions

Citation Information

If you use this dataset, please cite:

@dataset{livestock_health_synthetic_2024,
  author = {Electric Sheep Africa},
  title = {Livestock Health and Disease Surveillance Synthetic Dataset for Sub-Saharan Africa},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/electricsheepafrica/livestock-health-disease-ssa-synthetic}
}

Key Literature Sources

This dataset synthesizes information from 50+ sources, including:

• Perry & Grace (2009): Economic impacts of animal diseases (Journal of Agricultural Economics)
• Cleaveland et al. (2001): Diseases of humans and domestic mammals (Phil Trans Royal Society B)
• Leonard et al. (2017): Veterinary service delivery in developing countries (Rev. sci. tech. Off. int. Epiz)
• Robinson et al. (2011): Global livestock production systems (FAO/ILRI)
• AU-IBAR (2013): Veterinary services delivery in Africa (African Union)
• McCorkle (1995): Ethnoveterinary R&D (Agriculture and Human Values)
• Herrero et al. (2013): Biomass use in global livestock systems (PNAS)
• Reid et al. (2014): Pastoral land development models (Ecology and Society)

Full bibliography available in parameter files (parameters_livestock/ directory).

Dataset Structure

Variable Types

• Categorical (9 variables): Zones, disease types, systems
• Continuous (14 variables): Herd sizes, distances, indices, rates
• Binary (4 variables): Access, incidence, supplementation

Sample Record

agro_ecological_zone,region_type,herd_size_cattle,disease_incidence_annual,vet_distance_km,...
semi_arid,rural_accessible,4,yes,35.2,...

Updates and Versioning

• Version: 1.0
• Release Date: November 2024
• Status: Stable
• Planned Updates: None currently planned

Contact

Creator: Electric Sheep Africa
Repository: GitHub
Issues: Report via GitHub Issues

License

CC BY 4.0 (Creative Commons Attribution 4.0 International)

You are free to:

• ✅ Share and redistribute
• ✅ Adapt and build upon
• ✅ Use commercially

Under the condition that you:

• ✅ Give appropriate credit
• ✅ Indicate if changes were made
• ✅ Do not misrepresent as real surveillance data

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How to Load

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("electricsheepafrica/livestock-health-disease-ssa-synthetic")

# Load as pandas DataFrame
import pandas as pd
df = dataset['train'].to_pandas()

# Or load Parquet directly
df = pd.read_parquet("livestock_data.parquet")

Example Use Cases

1. Disease Risk Prediction

# Train ML model to predict disease incidence
X = df[['herd_size_cattle', 'vet_distance_km', 'vaccination_coverage_pct', 
        'agro_ecological_zone', 'pasture_quality_index']]
y = df['disease_incidence_annual']

2. Vet Clinic Placement Optimization

# Find underserved areas
underserved = df[(df['vet_distance_km'] > 60) & (df['livestock_tlu'] > 5)]

3. Vaccination Campaign Targeting

# Identify high-risk, low-coverage households
targets = df[(df['vaccination_coverage_pct'] < 20) & 
             (df['disease_incidence_annual'] == 'yes')]

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Dataset 2 of 5 in the African Agriculture & Food Security Synthetic Data Portfolio