  Accurate epidemic forecasting plays a vital role for governments in
developing effective prevention measures for suppressing epidemics. Most of the
present spatio-temporal models cannot provide a general framework for stable,
and accurate forecasting of epidemics with diverse evolution trends.
Incorporating epidemiological domain knowledge ranging from single-patch to
multi-patch into neural networks is expected to improve forecasting accuracy.
However, relying solely on single-patch knowledge neglects inter-patch
interactions, while constructing multi-patch knowledge is challenging without
population mobility data. To address the aforementioned problems, we propose a
novel hybrid model called Metapopulation-based Spatio-Temporal Attention
Network (MPSTAN). This model aims to improve the accuracy of epidemic
forecasting by incorporating multi-patch epidemiological knowledge into a
spatio-temporal model and adaptively defining inter-patch interactions.
Moreover, we incorporate inter-patch epidemiological knowledge into both the
model construction and loss function to help the model learn epidemic
transmission dynamics. Extensive experiments conducted on two representative
datasets with different epidemiological evolution trends demonstrate that our
proposed model outperforms the baselines and provides more accurate and stable
short- and long-term forecasting. We confirm the effectiveness of domain
knowledge in the learning model and investigate the impact of different ways of
integrating domain knowledge on forecasting. We observe that using domain
knowledge in both model construction and loss functions leads to more efficient
forecasting, and selecting appropriate domain knowledge can improve accuracy
further.
