SYSTEM DOCUMENTATION & REQUIREMENTS
💡 This Skill requires geopandas, pysal, scikit-learn, numpy, pandas, shapely, scipy, matplotlib, loguru, fastapi, uvicorn, pydantic, pydantic-settings, pyproj, rasterio, fiona, folium, plotly, networkx, osmnx, dask, xarray, xgboost, tensorflow, torch, requests, httpx, websockets, python-multipart, pyyaml, black, isort, flake8, mypy, pytest, pytest-asyncio, pytest-cov, hypothesis, and includes scripts (resource) and references (resource) and assets (resource) components.
What problem does it solve?
This Skill addresses the challenge of understanding and acting upon health-related data by providing advanced spatial analysis and epidemiological tools, enabling better public health decision-making.
Core Features & Use Cases
- Disease Surveillance: Identify disease hotspots and track outbreaks using spatial clustering and incidence rate calculations.
- Healthcare Accessibility: Analyze how easily populations can access healthcare facilities based on location, services, and travel time.
- Environmental Health: Assess the impact of environmental factors like air quality on public health outcomes.
- Use Case: A public health official can use this Skill to map areas with high rates of a specific disease, identify nearby hospitals with available emergency services, and analyze the correlation between local air quality and respiratory illness clusters.
Quick Start
Use the geo-infer-health skill to find disease hotspots within a 5km radius of the provided coordinates.
