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COVID-19 infection inference with graph neural networks
Kyungwoo Song1, Hojun Park2, Junggu Lee3
1Department of Applied Statistics, Yonsei University, Seoul, 03722, Republic of Korea.
Scientific Reports
|July 15, 2023
Summary
This study developed an automatic tool using graph neural networks to predict COVID-19 spread. Incorporating contact information significantly improved the accuracy of identifying future infections.
Area of Science:
- Epidemiology
- Machine Learning
- Network Science
Background:
- Infectious diseases necessitate efficient epidemiological surveys to identify high-risk transmitters and reduce transmission.
- Current methods can be burdensome for epidemiologists, highlighting the need for automated tools.
Purpose of the Study:
- To develop an automated epidemiological survey tool to predict the impact of COVID-19 infected individuals on future infections.
- To leverage graph neural networks (GNNs) for enhanced prediction accuracy.
Main Methods:
- Utilized a dataset of confirmed COVID-19 cases, including interaction details (contact order, times, routes) and individual properties (symptoms).
- Applied two GNN variants: graph convolutional networks and graph attention networks.
- Compared GNN performance against traditional machine learning models.
Main Results:
- Graph-based models demonstrated superior performance compared to traditional machine learning approaches.
- Area under the curve for 2nd, 3rd, and 4th order spreading predictions improved by 0.200, 0.269, and 0.190, respectively.
- Contact information was identified as a critical factor in predicting future infections.
Conclusions:
- The contact network of infected individuals is crucial for predicting their influence on future transmissions.
- Integrating relational data within an automated survey significantly enhances epidemiological prediction effectiveness.
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