Infectious Disease Surveillance Dashboard
Real-time epidemiological monitoring - New Jersey
🏆 Top 10 Diseases by Case Volume
🗺️ Disease Burden by County
📋 Detailed Case Report by County & Disease
| County | Disease / Pathogen | Case Count | Severity Level |
|---|
📈 Predictive Modeling: 30-Day Forward Projection
Time-series forecast based on current epidemiological growth rates and historical incidence data.
📊 Top 5 Diseases - Forward Projections
⚠️ Risk Factors Assessment (Simulated)
Clinical & Public Health Recommendations
Evidence-based interventions triggered by critical thresholds
💰 Resource Allocation Strategy
📉 Projected Intervention Impact
📊 Executive Summary - Technical Analysis
The dashboard presents a coherent data architecture for epidemiological surveillance, with indicators well correlated to the operational needs of health officials. The business logic is solid, but some methodological biases deserve to be documented for rigorous interpretation.
1. Indicator Logic & Business Correlations
| Indicator | Operational Need | Correlation |
|---|---|---|
| Total Cases | Situation awareness | Strong ✅ |
| Predominant Disease | Resource prioritization | Very strong ✅✅ |
| Most Affected County | Geographic targeting | Very strong ✅✅ |
| 30-day Projection | Anticipatory planning | Moderate ⚠️ |
2. Metrics Analysis - Technical Details
Identified Biases: Population effect (Populous counties like Middlesex are systematically overrepresented).
Suggested Correction: Add an incidence rate indicator per 100k inhabitants.
3. Predictive Model - Critical Analysis
Current Strengths: Total transparency, user adjustable.
Major Weaknesses: No seasonality, no saturation effect, spatial correlation neglected.
Suggested Improvement: Implement a Susceptible-Infectious-Recovered (SIR) dynamic model.
4. Statistical Risk & Data Quality
The dashboard treats all data as equally reliable. In reality, diseases face severe under-reporting (e.g., Lyme disease ~70%). Raw counts should be adjusted using pathogen-specific reporting rates. We also recommend adding a Data Confidence score and anomaly detection for zero-inflation.
5. Confidence Rating
- Calculation accuracy: 95%
- Business consistency: 88%
- Predictive robustness: 65%
- Actionability: 85%