Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction
Large-scale infectious diseases pose a tremendous risk to humans, with global outbreaks of COVID-19 causing millions of deaths and trillions of dollars in economic losses. To minimize the damage caused by large-scale infectious diseases, it is necessary to develop infectious disease prediction models to provide assistance for prevention. In this paper, the authors propose an XGBoost-LSTM mixed framework that predicts the spread of infectious diseases in multiple cities and regions. According to big traffic data, it was found that population flow is closely related to the spread of infectious diseases. Clustering and dividing cities according to population flow can significantly improve prediction accuracy. Meanwhile, an XGBoost is used to predict the transmission trend based on the key features of infection. An LSTM is used to predict the transmission fluctuation based on infection-related multiple time series features. The mixed model combines transmission trends and fluctuations to predict infections accurately. The proposed method is evaluated on a dataset of highly pathogenic infectious disease transmission published by Baidu and compared with other advanced methods. The results show that the model has an excellent predictive effect and practical value for large-scale infectious disease prediction.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
-
Supplemental Notes:
- Copyright © 2024, IEEE.
-
Authors:
- Guo, Kehua
- Shen, Changchun
- Zhou, Xiaokang
- Ren, Sheng
- Hu, Min
- Chen, Xingyu
- Shen, Minxue
- Guo, Haifu
- Publication Date: 2024-1
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 774-785
-
Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 25
- Issue Number: 1
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Communicable diseases; COVID-19; Neural networks; Predictive models; Traffic data
- Identifier Terms: XGBoost (Software)
- Subject Areas: Data and Information Technology; Highways; Security and Emergencies;
Filing Info
- Accession Number: 01922519
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 24 2024 11:25AM