TY - JOUR AU - Wang, Lijing AU - Chen, Jiangzhuo AU - Marathe, Madhav PY - 2019/07/17 Y2 - 2024/03/28 TI - DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - IAAI Technical Track: Emerging Papers DO - 10.1609/aaai.v33i01.33019607 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5023 SP - 9607-9612 AB - <p>Influenza-like illness (ILI) is among the most common diseases worldwide. Producing timely, well-informed, and reliable forecasts for ILI is crucial for preparedness and optimal interventions. In this work, we focus on short-term but highresolution forecasting and propose DEFSI (<em>D</em>eep Learning Based <em>E</em>pidemic <em>F</em>orecasting with <em>S</em>ynthetic <em>I</em>nformation), an epidemic forecasting framework that integrates the strengths of artificial neural networks and causal methods. In DEFSI, we build a two-branch neural network structure to take both within-season observations and between-season observations as features. The model is trained on geographically highresolution synthetic data. It enables detailed forecasting when high-resolution surveillance data is not available. Furthermore, the model is provided with better generalizability and physical consistency. Our method achieves comparable/better performance than state-of-the-art methods for short-term ILI forecasting at the state level. For high-resolution forecasting at the county level, DEFSI significantly outperforms the other methods.</p> ER -