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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / AAAI-21 Special Programs and Special Track

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

February 1, 2023

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Authors

Dongdong Wang

University of Central Florida


Shunpu Zhang

University of Central Florida


Liqiang Wang

University of Central Florida


DOI:

10.1609/aaai.v35i17.17812


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.

Topics: AAAI

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HOW TO CITE:

Dongdong Wang||Shunpu Zhang||Liqiang Wang Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19 Proceedings of the AAAI Conference on Artificial Intelligence (2021) 15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19 AAAI 2021, 15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang (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, 15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang. Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang. 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". 15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang. (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, p.15424-15430

Dongdong Wang||Shunpu Zhang||Liqiang Wang, "Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19", AAAI, p.15424-15430, 2021.

Dongdong Wang||Shunpu Zhang||Liqiang Wang. "Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang. "Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 15424-15430.

Dongdong Wang||Shunpu Zhang||Liqiang Wang. Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19. AAAI[Internet]. 2021[cited 2023]; 15424-15430.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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