HG-DCM: History Guided Deep Compartmental Model for Early Stage Pandemic Forecasting
Wei, Z.; Li, M. L.
Show abstract
We introduce the History-Guided Deep Compartmental Model (HG-DCM), a novel framework for early-stage pandemic forecasting that synergizes deep learning with compartmental modeling to harness the strengths of both interpretability and predictive capacity. HG-DCM employs a Residual Convolutional Neural Network (RCNN) to learn temporal patterns from historical and current pandemic data while incorporating epidemiological and demographic metadata to infer interpretable parameters for a compartmental model to forecast future pandemic growth. Experimental results on early-stage COVID-19 and Monkeypox forecasting tasks demonstrate that HG-DCM outperforms both standard compartmental models (e.g., DELPHI) and standalone deep neural networks (e.g., GRU) in predictive accuracy and stability, particularly with limited data. By effectively integrating historical pandemic insights, HG-DCM offers a scalable approach for interpretable and accurate forecasting, laying the groundwork for future real-time pandemic modeling applications.
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