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An Automated Analysis Framework for Epidemiological Survey on COVID-19
IEEE Journal of Biomedical and Health Informatics
|February 27, 2024
Summary
This study introduces an automated visual epidemiological survey analysis (AVESA) framework to streamline COVID-19 data analysis. AVESA significantly improves efficiency and accuracy compared to manual methods, enhancing epidemiological insights.
Area of Science:
- Epidemiology
- Artificial Intelligence
- Data Science
Background:
- COVID-19 prevention and control rely heavily on epidemiological surveys and case report analysis.
- Current manual analysis of epidemiological data is time-consuming and labor-intensive, hindering efficient disease control.
Purpose of the Study:
- To design and evaluate an automated visual epidemiological survey analysis (AVESA) framework for COVID-19.
- To improve the efficiency and accuracy of analyzing epidemiological survey reports.
Main Methods:
- Developed a deep neural network for information extraction from COVID-19 case reports.
- Constructed an epidemiological knowledge graph using predefined patterns.
- Implemented a multi-dimensional knowledge reasoning model for graph analysis.
- Applied weighted PageRank and Louvain algorithms for risk and community analysis.
Main Results:
- Achieved F1 scores of 85.12% (entity extraction) and 92.29% (multi-task extraction), outperforming standalone models.
- Full-graph computing results closely aligned with manual analysis standards.
- Weighted PageRank improved Top_Recall_n% by 11.21%; weighted Louvain showed a 4.34% community difference rate compared to manual analysis.
Conclusions:
- The AVESA framework offers an efficient and accurate automated solution for COVID-19 epidemiological analysis.
- Automated knowledge graph construction and reasoning significantly enhance epidemiological survey analysis.
- AVESA demonstrates superior performance in risk and community detection compared to traditional methods.
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