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Accelerating Epidemiological Investigation Analysis by Using NLP and Knowledge Reasoning: A Case Study on COVID-19
Summary
This study introduces an automated framework to analyze COVID-19 epidemiological reports, significantly speeding up the identification of infection sources and contacts. The system uses a novel neural network and knowledge graph for efficient public health surveillance.
Area of Science:
- Epidemiology
- Artificial Intelligence
- Public Health
Background:
- COVID-19 poses a significant global health threat, necessitating efficient epidemiological investigations.
- Manual analysis of extensive case reports for contact tracing and source identification is time-consuming and labor-intensive.
- Effective control of infectious disease spread relies on rapid and accurate epidemiological data analysis.
Purpose of the Study:
- To develop an automated framework for analyzing epidemiological case reports.
- To expedite the identification of infection sources, modes, and pathways for diseases like COVID-19.
- To create a scalable system for public health surveillance and response.
Main Methods:
- Implementation of a Tuple-based Multi-Task Neural Network (TMT-NN) for joint entity and relation recognition.
- Construction of an epidemiological knowledge graph to represent and store investigation data.
- Development of an inference engine to deduce infection dynamics from the knowledge graph.
- Creation and release of a real-world COVID-19 epidemiological investigation dataset.
Main Results:
- The TMT-NN model demonstrated promising performance in recognizing epidemiological entities and relations.
- The developed framework successfully automated key aspects of epidemiological case report analysis.
- A comprehensive COVID-19 epidemiological knowledge graph was established and shared.
- The study released a valuable dataset for further research in epidemiological analysis.
Conclusions:
- Automated analysis of epidemiological reports using AI significantly enhances the efficiency of disease surveillance.
- The proposed framework and knowledge graph provide a robust tool for understanding and controlling infectious disease outbreaks.
- Sharing the dataset and knowledge graph fosters collaborative research and advancements in public health informatics.
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