🧪 Try It Yourself

• 📈 Upload a CSV file with two columns: dates in the first column and disease mention counts in the second
• 🎯 Click "Plot Time Series" to visualize your data
• 🔍 Select an anomaly detection method from the dropdown
• ⚙️ Configure the detection parameters:
  • For LSTM method:
    • k: Controls sensitivity (1-3). Higher values mean stricter anomaly detection.
    • Percentile: Threshold percentile for anomaly detection (0-100).
    • Threshold Method: Choose how to calculate anomaly thresholds:
      • IQR-based methods: Compare predictions with actual values using different metrics
      • Percentile-based methods: Use statistical thresholds on prediction errors
  • For ARIMA method:
    • k: Sensitivity multiplier for standard deviation-based thresholds (1-3).
  • For IQR method:
    • k: IQR multiplier (1-3). Higher values detect more extreme outliers.
• ⚡ Click "Detect Anomalies" to identify unusual patterns in your time series

This tool combines time series analysis and anomaly detection to help identify potential disease outbreaks based on news coverage patterns. The results can be used to alert public health officials about emerging health concerns. 💡