EINNs: Epidemiologically-Informed Neural Networks
EINNs: Epidemiologically-Informed Neural Networks
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DOI：
10.1609/aaai.v37i12.26690
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Alexander Rodr'iguez;Jiaming Cui;Naren Ramakrishnan;B. Adhikari;B. Prakash
中科院分区：
文献类型：
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作者：
Alexander Rodr'iguez;Jiaming Cui;Naren Ramakrishnan;B. Adhikari;B. Prakash
我们介绍了EINNs，这是一个为流行病预测而精心设计的框架，它建立在机制模型提供的理论基础上，以及人工智能模型提供的数据驱动的可表达性，以及它们摄取异构信息的能力。尽管神经预测模型在多个任务中取得了成功，但与流行病趋势和长期预测密切相关的预测仍然是开放的挑战。流行病学ODE模型包含可以指导我们完成这两项任务的机制；然而，它们摄取数据源和建模复合信号的能力有限。因此，我们建议利用物理信息神经网络中的工作来学习潜在的流行病动力学，并将相关知识转移到另一个摄取多个数据源并具有更适当归纳偏差的神经网络中。与以前的工作不同，我们没有假设完全动力学的可观察性，也不需要在训练过程中对ODE方程进行数值求解。我们在美国所有州和卫生与公众服务部地区进行的COVID-19和流感预测的彻底实验表明，我们的方法在短期和长期预测以及学习其他重要替代方案的机制动力学方面都具有明显的优势。
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information. Although neural forecasting models have been successful in multiple tasks, predictions well-correlated with epidemic trends and long-term predictions remain open challenges. Epidemiological ODE models contain mechanisms that can guide us in these two tasks; however, they have limited capability of ingesting data sources and modeling composite signals. Thus, we propose to leverage work in physics-informed neural networks to learn latent epidemic dynamics and transfer relevant knowledge to another neural network which ingests multiple data sources and has more appropriate inductive bias. In contrast with previous work, we do not assume the observability of complete dynamics and do not need to numerically solve the ODE equations during training. Our thorough experiments on all US states and HHS regions for COVID-19 and influenza forecasting showcase the clear benefits of our approach in both short-term and long-term forecasting as well as in learning the mechanistic dynamics over other non-trivial alternatives.