Title: HG-DCM: History Guided Deep Compartmental Model for Early Stage Pandemic Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Pandemic Forecasting, Time-Series Forecasting, Deep Compartmental Modeling, Explainable AI, Epidemiological Modeling

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 (CNN) 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 forecasting tasks demonstrate that HG-DCM outperforms both standard compartmental models (e.g., DELPHI) and standalone deep neural networks (e.g., CNN) 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.

TL;DR: We introduce HG-DCM, a history-guided deep compartmental model that integrates neural networks with epidemiological modeling to deliver interpretable and more accurate early-stage pandemic forecasts.