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.

我们介绍了 EINNs，这是一个基于机械模型提供的理论基础和人工智能模型提供的数据驱动的表达能力，以及摄入异构信息的能力，用于流行病预测的框架。通过物理 - 信息神经网络的工作来学习潜在的流行病动态并将相关知识转移给另一个神经网络，这个网络摄入多个数据源，并具有更适当的归纳偏差。我们的全面实验表明，与其他非平凡的选择相比，在 COVID-19 和流感预测的所有美国州和 HHS 地区都展示了我们方法的明显优势。