DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information
DEFSI: Deep Learning Based Epidemic Forecasting with Synthetic Information
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DEFSI：基于深度学习的综合信息流行病预测
DOI：
10.1609/aaai.v33i01.33019607
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发表时间：
2019
影响因子：
0.7
通讯作者：
M. Marathe
中科院分区：
文献类型：
--
作者：
Lijing Wang;Jiangzhuo Chen;M. Marathe
流感样疾病（ILI）是世界上最常见的疾病之一。对ILI进行及时、充分和可靠的预测对于备灾和最佳干预至关重要。在这项工作中，我们专注于短期但高分辨率的预测，并提出了DEFSI（基于深度学习的综合信息流行病预测），这是一个集成了人工神经网络和因果方法优势的流行病预测框架。在DEFSI中，我们建立了一个两个分支的神经网络结构，以季节内观测和季节间观测作为特征。该模型是在地理高分辨率合成数据上训练的。它可以在没有高分辨率监测数据的情况下进行详细的预测。该模型具有较好的泛化能力和物理一致性。我们的方法实现了可比/更好的性能比国家的最先进的方法在国家一级的短期ILI预测。对于县级的高分辨率预报，DEFSI显著优于其他方法。
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 (Deep Learning Based Epidemic Forecasting with Synthetic Information), 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.