Abstract:

Major sudden infectious diseases are often characterized by high infectivity， rapid mutation and significant risk， which pose substantial threats to human life security and economic development. Epidemiological investigation is a crucial step in curbing the spread of infectious diseases and are prerequisites for implementing precise full-chain infection prevention and control measures. Existing epidemiological investigation systems have many shortcomings， such as manual inefficiencies， poor data quality， and lack of specialized knowledge. To address these defects， a set of technological application schemes were proposed to assist in epidemiological investigation based on the existing digitization combined with knowledge graph. Firstly， a knowledge graph was constructed on the basis of the five categories of entities： people， locations， events， items， and organizations， as well as their relationships and attributes. Secondly， following the idea of identifying risk points and tracing to close contacts based on cases， cases were used as the starting point with points as the focuses to aid in determining at-risk populations and points risk. Finally， through the visual analysis of epidemiological investigation data， several applications were implemented， including information placement in epidemiological investigation， tracing of the spread and propagation， and the awareness of epidemic situations， so as to assist in the successful implementation of major sudden infectious disease prevention and control work. Within the same error range， the accuracy of the graph enhancement-based trajectory placement method is significantly higher than that of the traditional manual inquiry-based method， with the determination accuracy within one kilometer reached 85.15%； the graph enhancement-based method for determining risk points and populations improves the efficiency significantly， reducing the average time to generate reports to within 1 h. Experimental results demonstrate that the proposed scheme integrates the technical advantages of knowledge graph effectively， improves the scientific nature and effectiveness of precise epidemic prevention and control strategy formulation， and provides important reference value for practical exploration in the field of infectious disease prevention.

Key words: infectious disease, epidemiological investigation, knowledge graph, risk prevention and control, assistant decision-making

摘要：

重大突发性传染病以它的强传染性、快变异性和高风险性，对人类生命安全与经济发展构成重大威胁。流行病学调查是遏制传染病扩散的关键步骤和落实全链路精准防控的前提。针对现有流调系统存在的人工效率低下、数据质量差、专业知识不足等问题，在现有数字化的基础上结合知识图谱，提出一套辅助流行病学调查的技术应用方案。首先，基于人、地、事、物、组织五大类实体及其关系和属性构建知识图谱；其次，根据病例查风险点位查密接的思路，以病例为起点，以点位为重心，辅助判定风险人群和风险点位；最后，通过对流调数据的可视化分析，实现流调信息落位、传播扩散溯源和疫情态势感知等多个应用，从而辅助重大突发性传染病防控工作的顺利开展。在相同的误差范围内，基于图谱增强的轨迹落位方法的准确率显著高于传统基于人工问询的方法，千米内的判定准确率达到85.15%；基于图谱增强的风险点位和人群的判定方法使得效率显著提升，生成报告的平均耗时降至1 h内。实验结果表明，所提方案有效融合了知识图谱的技术优势，不仅提高了精准疫情防控策略制定的科学性与时效性，更为流行病传染预防领域的实践探索提供了重要的参考价值。

关键词: 传染病, 流行病学调查, 知识图谱, 风险防控, 辅助决策