research-article

Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea

Authors:

Jae-Gil LeeAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 3466 - 3473

https://doi.org/10.1145/3394486.3412864

Published: 20 August 2020 Publication History

Abstract

The escalating crisis of COVID-19 has put people all over the world in danger. Owing to the high contagion rate of the virus, COVID-19 cases continue to increase globally. To further suppress the threat of the COVID-19 pandemic and minimize its damage, it is imperative that each country monitors inbound travelers. Moreover, given that resources for quarantine are often limited, they must be carefully allocated. In this paper, to aid in such allocation by predicting the number of inbound COVID-19 cases, we propose Hi-COVIDNet, which takes advantage of the geographic hierarchy. Hi-COVIDNet is based on a neural network with two-level components, namely, country-level and continent-level encoders, which understand the complex relationships among foreign countries and derive their respective contagion risk to the destination country. An in-depth case study in South Korea with real-world COVID-19 datasets confirmed the effectiveness and practicality of Hi-COVIDNet.

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The escalating crisis of COVID-19 has put people all over the world in danger. Owing to the high contagion rate of the virus, COVID-19 cases continue to increase globally. To further suppress the threat of the COVID-19 pandemic and minimize its damage, it is imperative that each country monitors inbound travelers. Moreover, given that resources for quarantine are often limited, they must be carefully allocated. In this paper, to aid in such allocation by predicting the number of inbound COVID-19 cases, we propose Hi-COVIDNet, which takes advantage of the geographic hierarchy. Hi-COVIDNet is based on a neural network with two-level components, namely, country-level and continent-level encoders, which understand the complex relationships among foreign countries and derive their respective contagion risk to the destination country. An in-depth case study in South Korea with real-world COVID-19 datasets confirmed the effectiveness and practicality of Hi-COVIDNet.

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Wang BHe XLin HShen GKong X(2024)An Epidemic Trend Prediction Model with Multi-source Auxiliary DataWeb and Big Data10.1007/978-981-97-7244-5_19(286-301)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7244-5_19
Wang CMustafa S(2023)A data-driven Markov process for infectious disease transmissionPLOS ONE10.1371/journal.pone.028989718:8(e0289897)Online publication date: 10-Aug-2023
https://doi.org/10.1371/journal.pone.0289897
Zhu XHuang XSun LLiu J(2023)A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission ClustersACM Transactions on Knowledge Discovery from Data10.1145/353849217:2(1-24)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3538492
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Index Terms

Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

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Published: 20 August 2020

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Cited By

Wang BHe XLin HShen GKong X(2024)An Epidemic Trend Prediction Model with Multi-source Auxiliary DataWeb and Big Data10.1007/978-981-97-7244-5_19(286-301)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7244-5_19
Wang CMustafa S(2023)A data-driven Markov process for infectious disease transmissionPLOS ONE10.1371/journal.pone.028989718:8(e0289897)Online publication date: 10-Aug-2023
https://doi.org/10.1371/journal.pone.0289897
Zhu XHuang XSun LLiu J(2023)A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission ClustersACM Transactions on Knowledge Discovery from Data10.1145/353849217:2(1-24)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3538492
Trirat PYoon SLee J(2023)MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.323707224:4(3779-3794)Online publication date: Apr-2023
https://doi.org/10.1109/TITS.2023.3237072
Zhang SBi GQi JYang YKong XNan FZhou MYang P(2023)Privacy-Preserving-Enabled Lightweight COVID-19 Simulation Model for Mobile Intelligent ApplicationIEEE Internet of Things Journal10.1109/JIOT.2022.316268710:8(6742-6755)Online publication date: 15-Apr-2023
https://doi.org/10.1109/JIOT.2022.3162687
Negreiros RSilva IAlves AValadares DPerkusich ABaptista C(2023)COVID-19 Diagnosis Through Deep Learning Techniques and Chest X-Ray ImagesSN Computer Science10.1007/s42979-023-02043-14:5Online publication date: 11-Aug-2023
https://doi.org/10.1007/s42979-023-02043-1
Cao LCao L(2023)Modeling MethodsGlobal COVID-19 Research and Modeling10.1007/978-981-99-9915-6_10(185-240)Online publication date: 18-Dec-2023
https://doi.org/10.1007/978-981-99-9915-6_10
Stylianides CMalialis KKolios P(2023)A Study of Data-Driven Methods for Adaptive Forecasting of COVID-19 CasesArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44207-0_6(62-74)Online publication date: 22-Sep-2023
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Li YWang YMa K(2022)Integrating Transformer and GCN for COVID-19 ForecastingSustainability10.3390/su14161039314:16(10393)Online publication date: 21-Aug-2022
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Jang YKim MLee CChoi JLee JLee SSung T(2022)A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 PandemicInternational Journal of Environmental Research and Public Health10.3390/ijerph1911676319:11(6763)Online publication date: 1-Jun-2022
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