Synthetic Data Augmentation for Epidemic Forecasting

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Using computer-generated time-series data reflective of an epidemic’s dynamics to augment or replace limited real-world case data during the initial stages of an outbreak.

Why It Matters

Demonstrates that synthetically generated epidemic time-series data can outperform real historical data alone for training deep learning models, providing a method to overcome initial data scarcity.

Evidence

models trained with synthetic data have better forecast accuracy than models trained on real data alone

Related Papers

• openalex-2603.24474-leveraging-synthetic-and-genetic-data-to-improve-epidemic-fo