Venesa123/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. 1.agro_ecological_zone: Arid, semi-arid, sub-humid, humid, highland
2. 2.region_type: Urban, peri-urban, rural accessible, rural remote
3. 3.farm_size_ha: Farm size in hectares
4. 4.soil_quality_index: Soil quality (0-100 scale)
5. 5.rainfall_mm_annual: Annual rainfall (mm)
6. 6.household_size: Number of household members
7. 7.market_distance_km: Distance to nearest market
8. 8.livestock_tlu: Tropical Livestock Units owned
9. 9.extension_access: Access to agricultural extension (yes/no)
10. 10.fertilizer_use_kg_ha: Fertilizer application rate
11. 11.rainfall_mm_season: Seasonal rainfall (mm)
12. 12.maize_yield_kg_ha: Maize yield (kg/ha)

Livestock Health & Production (NEW - 15 variables)

Herd Composition

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

Veterinary Services & Access

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

Disease & Health

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

Management Systems

1. 1.grazing_system: Type of grazing (communal, private, mixed, zero-grazing)
2. 2.water_source_reliability: Water availability (year-round, seasonal, unreliable)
3. 3.treatment_access: Type of treatment accessed (none, traditional, veterinary, both)
4. 4.feed_supplementation: Provides supplementary feed (yes/no)
5. 5.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. 1.Surveillance gaps: Most countries lack systematic disease surveillance
2. 2.Underreporting: Livestock diseases often go unreported (especially in remote areas)
3. 3.Fragmented data: Information scattered across vet clinics, ministries, NGOs
4. 4.Access restrictions: Sensitive disease data rarely shared publicly
5. 5.High collection costs: Surveys expensive and logistically challenging
6. 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. 1.Oversimplified disease dynamics: Real disease spread is more complex than modeled
2. 2.Static snapshot: No temporal dynamics (outbreaks, seasonality within year)
3. 3.No spatial clustering: Real diseases show geographic clustering not captured
4. 4.Coarse zones: 5 AEZ categories don't capture local variation
5. 5.Missing variables: No breed info, no herd demographics, no animal-level data
6. 6.Treatment outcomes: No data on treatment success/failure
7. 7.No cost data: Disease impacts measured only in mortality, not economics
8. 8.Simplified grazing: Complex pastoral mobility patterns simplified
9. 9.Binary disease incidence: Real incidence is more granular (multiple episodes)

Potential Biases

1. 1.Literature bias: Sources mostly from East Africa (Kenya, Tanzania, Ethiopia)
2. 2.Veterinary access: May overestimate coverage in very remote pastoral areas
3. 3.Disease reporting: Literature likely underrepresents mild/unreported diseases
4. 4.Poultry systems: Village chickens well-represented, commercial systems underrepresented
5. 5.Traditional knowledge: Traditional treatment effectiveness may be under-captured
6. 6.Gender: No gender disaggregation of livestock ownership/management
7. 7.Wealth gradient: Livestock wealth distribution may be too uniform
8. 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

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')]

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