Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19

Authors

  • Dongdong Wang University of Central Florida
  • Shunpu Zhang University of Central Florida
  • Liqiang Wang University of Central Florida

Keywords:

COVID-19, Blackbox Knowledge Distillation, Mixup, Epidemiological Modeling

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.

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Published

2021-05-18

How to Cite

Wang, D., Zhang, S., & Wang, L. (2021). Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15424-15430. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17812

Issue

Section

IAAI Technical Track on Emerging Applications of AI