| name | geo-infer-health |
| description | Spatial epidemiology and public health analysis. Use when modeling disease spread, analyzing health disparities, performing spatial health risk assessment, building epidemiological surveillance systems, or assessing healthcare accessibility. |
| prerequisites | {"required":["geo-infer-space","geo-infer-data"],"recommended":["geo-infer-bayes","geo-infer-time"]} |
| difficulty | intermediate |
| estimated_time | 45min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-HEALTH
Instructions
Core Capabilities
- Spatial epidemiology: Disease clustering (SaTScan), hotspot detection, SIR/SEIR spatial models
- Health disparities: Accessibility analysis, equity mapping, deprivation indices
- Risk assessment: Environmental health risk, exposure modeling
- Surveillance: Real-time epidemiological monitoring, early warning systems
- Accessibility: Hospital catchment areas, travel time to care, coverage gaps
- Data validation: Coordinate precision checks (flags >6 decimal places as suspect)
Key Imports
from geo_infer_health.core.epidemiology import EpidemiologicalModel
from geo_infer_health.core.risk_assessment import HealthRiskAssessor
from geo_infer_health.core.accessibility import HealthcareAccessAnalyzer
from geo_infer_health.utils.advanced_geospatial import SpatialValidator
Examples
from geo_infer_health.core.epidemiology import EpidemiologicalModel
model = EpidemiologicalModel(disease_type="infectious")
clusters = model.detect_clusters(cases_gdf, method="satscan")
risk_surface = model.compute_risk_surface(clusters, population_raster)
Guidelines
- Coordinate validation checks for unrealistic precision (>6 decimal places)
Integrations
- Integrates with SPACE for H3-based health district tessellation
- Integrates with TRANSPORT for healthcare accessibility travel times
- Test:
uv run python -m pytest GEO-INFER-HEALTH/tests/ -v
