Spatial-Temporal Analysis of COVID-19 Transmission Based on Geo-Location Linked Data

被引：0
|
作者：
Ying S. [1 ,2 ]
Xu Y. [1 ]
Dou X. [1 ]
Chen X. [2 ,3 ]
Zhao J. [2 ,3 ]
Guo H. [2 ,3 ]
机构：
[1] School of Resource and Environmental Sciences, Wuhan University, Wuhan
[2] Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen
[3] Shenzhen Research Center of Digital City Engineering, Shenzhen
来源：
| 1600年 / Wuhan University卷 / 45期
关键词：
Coronavirus disease 2019(COVID-19); Epidemiological investigation; Five-tuple model; Geo-location linked analysis; Spatial-temporal analysis;
D O I：
10.13203/j.whugis20200241
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.
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页码：798 / 807
页数：9
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