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Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Cali-Net to steer EpiDeep with COVID-related signals | EpiDeep-CN · COVID-wILI, T1 (EWs 9–11) | RMSE | Helped |
| Use attentive transfer to suppress misaligned COVID signals | Cali-Net · COVID-wILI, T2 (EWs 12–15) | RMSE | Helped |
| Use Cali-Net instead of historical forecasting baselines | COVID-wILI · T (EWs 9–15), 11 regions | regions with best performance: 5 out of 11; 10 out of 11 | Mixed |
| Add regional reconstruction (ablation) | Caem · COVID-wILI | RMSE | Helped |
| Add Laplacian regularization (ablation) | Caem · COVID-wILI | RMSE | Helped |
| Add the recurrent model (ablation) | Caem · COVID-wILI | RMSE | Helped |
| Add attentive knowledge distillation losses (ablation) | Cali-Net · COVID-wILI, 1-week ahead | RMSE | Helped |
| Add attentive knowledge distillation losses (ablation) | Cali-Net · COVID-wILI, long-term forecasting | RMSE | Hurt |
| Add line-list data (DS1) (ablation) | Cali-Net · COVID-wILI | average overall RMSE | Helped |
| Add testing data (DS2) (ablation) | Cali-Net · COVID-wILI | average overall RMSE | Mixed |
| Add crowdsourced symptom data (DS3) (ablation) | Cali-Net · COVID-wILI | average overall RMSE | Mixed |
Closest papers by meaning
- Transfer Graph Neural Networks for Pandemic Forecasting (AAAI 2021 · similarity 0.88)
- Dynamics-Based Feature Augmentation of Graph Neural Networks for Variant Emergence Prediction (AAAI 2025 · similarity 0.83)
- EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (ICML 2025 · similarity 0.82)
- Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint) (AAAI 2024 · similarity 0.81)
- Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer (ICML 2025 · similarity 0.80)
- Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models (NeurIPS 2026 · similarity 0.79)
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