A research application for occupational health risk assessment in agricultural populations using intelligent typological analysis.
This Streamlit application implements a comprehensive framework for identifying vulnerability archetypes among farming communities exposed to multiple environmental hazards. The tool combines ensemble clustering, predictive modeling, and synergy analysis to provide data-driven insights for public health interventions.
app.py- Main application interface with Streamlitco_exposure_vulnerability_model.py- Core analytical engine
Multi-Dimensional Analysis
- Composite exposure scoring (dermal, chemical, physiological)
- Ensemble clustering for archetype discovery
- Synergistic hazard interaction analysis
- Predictive health risk modeling
- Targeted intervention planning
Technical Implementation
- Automated feature engineering and data preprocessing
- K-means, Spectral, and Agglomerative clustering with consensus
- Random Forest, XGBoost, and ensemble predictive models
- Statistical significance testing for synergy effects
- Interactive visualization and reporting
pip install -r requirements.txt
streamlit run app.py- Upload CSV data with agricultural health survey information
- Engineer Features to create composite exposure scores
- Discover Archetypes using ensemble clustering
- Analyze Synergies between different hazard types
- Profile Vulnerabilities for each identified archetype
- Validate Models with ensemble machine learning
- Generate Report with comprehensive findings and recommendations
- Vulnerability archetypes with population distribution
- Synergy indices and risk amplification factors
- Predictive model performance metrics (AUC, precision, recall)
- Evidence-based intervention strategies
- Comprehensive analytical report
- Public health research and surveillance
- Occupational risk assessment
- Targeted intervention planning
- Agricultural safety programs
- Academic research in environmental health