  Population-level disease prediction estimates the number of potential
patients of particular diseases in some location at a future time based on
(frequently updated) historical disease statistics. Existing approaches often
assume the existing disease statistics are reliable and will not change.
However, in practice, data collection is often time-consuming and has time
delays, with both historical and current disease statistics being updated
continuously. In this work, we propose a real-time population-level disease
prediction model which captures data latency (PopNet) and incorporates the
updated data for improved predictions. To achieve this goal, PopNet models
real-time data and updated data using two separate systems, each capturing
spatial and temporal effects using hybrid graph attention networks and
recurrent neural networks. PopNet then fuses the two systems using both spatial
and temporal latency-aware attentions in an end-to-end manner. We evaluate
PopNet on real-world disease datasets and show that PopNet consistently
outperforms all baseline disease prediction and general spatial-temporal
prediction models, achieving up to 47% lower root mean squared error and 24%
lower mean absolute error compared with the best baselines.
