Abstract: An accurate and efficient forecasting system is imperative to the prevention
of emerging infectious diseases such as COVID-19 in public health. This system
requires accurate transient modeling, lower computation cost, and fewer
observation data. To tackle these three challenges, we propose a novel deep
learning approach using black-box knowledge distillation for both accurate and
efficient transmission dynamics prediction in a practical manner. First, we
leverage mixture models to develop an accurate, comprehensive, yet impractical
simulation system. Next, we use simulated observation sequences to query the
simulation system to retrieve simulated projection sequences as knowledge.
Then, with the obtained query data, sequence mixup is proposed to improve query
efficiency, increase knowledge diversity, and boost distillation model
accuracy. Finally, we train a student deep neural network with the retrieved
and mixed observation-projection sequences for practical use. The case study on
COVID-19 justifies that our approach accurately projects infections with much
lower computation cost when observation data are limited.