Spatial-Temporal Analysis of COVID-19 Transmission Based on Geo-Location Linked Data
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摘要: 新型冠状病毒肺炎(coronavirus disease 2019，COVID-19)暴发以来，许多研究对COVID-19的发生和发展进行了分析和预测，但鲜见采用GIS空间分析技术进行COVID-19流行病学调查的研究。不同于医学的病毒原理和大数据分析方法，将流行病学调查知识与位置关联分析相结合，利用五元组模型结构化每个病例数据，采用GIS时空交叠原理处理病例数据，定义统计分类和传播分析的五元组操作规则，实现了确诊病例的分时、分区统计以及输入型、接触型和聚集性病例的判断、挖掘和疾病传播过程的分析。结果显示，五元组结构及其操作规则可实现病例数据的计算机自动化处理，能够快速获取疫情发展状况，推演疫情传播过程。五元组模型结合时序图、关系图等可视化技术能够有效地分析和展示疾病、健康或卫生事件的分布和传播情况，为疾控机构快速掌握疫情传播状况提供支持。Abstract: Spatial-temporal analysis method provides technical support for epidemiological investigation. To analyse and demonstrate the transmission of coronavirus disease 2019(COVID-19), this paper takes the data of COVID-19 cases in Shenzhen as an example, combines epidemiological investigation knowledge with geo-location linked association analysis, and uses the spatial-temporal five-tuple model to structure and analyze the case data. Rules based on spatial-temporal five-tuple model for case type judgment and statistical analysis are defined, which can use spatial-temporal overlap principles to judge two types of cases, input cases and contact cases, and to make temporal statistics and zoning statistics about the confirmed cases. This paper defines the five-tuple model and its operation rules for judging and analyzing the epidemic gathering situation, which can use the principle of spatial-temporal overlap to judge and mine the epidemic gathering situation and to analyze its propagation process. Combined with GIS spatial-temporal visualization, the entire process of epidemic developments and transmission are displayed in the maps with interactive interface along with temporal series diagrams and social relationship diagrams. During the spreading stage of the epidemic situation, by updating the case data and implementing the analysis, the spatial-temporal five-tuple structure and its operating rules could be feasible to judge, deduce quickly and show the changing status of the epidemic simultaneously with their visualization. The spatial-temporal five-tuple model combined with visualization technology can effectively display the distribution and transmission of the diseases, health or hygiene events, and provide support for disease control agencies to understand and control the spread of epidemic conditions.
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图 2 地理位置关联的COVID-19传播时空分析框架
Figure 2. Spatial-Temporal Analysis Framework of COVID-19 Transmission Based on Geo-Location Linked Analysis
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图 3 深圳市分区和分时COVID-19确诊人数统计图
Figure 3. Statistics of Confirmed COVID-19 Cases at Different Districts and Different Time in Shenzhen
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图 4 深圳市COVID-19确诊患者时空分布图
Figure 4. Temporal and Spatial Distributions of COVID-19 Cases in Shenzhen
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图 5 COVID-19输入型病例与所有病例的空间分布对比图(截至2020-02-23)
Figure 5. Comparison of Spatial Distribution of COVID-19 Input Cases and All Cases (Till Feb. 23, 2020)
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图 7 COVID-19接触型病例与所有病例的空间分布对比图(截至2020-03-06)
Figure 7. Comparison of Spatial Distribution of COVID-19 Contact Cases and All Cases (Till Mar. 6, 2020)
表 1 时空五元组模型元素及示例
Table 1 Elements and Examples of Spatial-Temporal Five-Tuple Model
五元组 病例信息属性 示例 主体 病例号 病例231 活动 患者的行为或状态 探亲、逗留(仅限于位置在深圳的情况)、来深圳、发病、入院、接触 对象 与患者接触的对象 病例1(病例231父亲) 时间 患者进行活动的时间点或时间段 2020-01-23 位置 患者所在位置名称，经度，纬度 幸福小区，116.21, 28.33 
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