Visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity

Coronavirus disease 2019 (COVID-19) is continuing to spread globally and still poses a great threat to human health. Since its outbreak, it has had catastrophic effects on human society. A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information. This analysis reveals the spread of the epidemic, from the perspective of spatio-temporal objects, to provide references for related research and the formulation of epidemic prevention and control measures. The case information is abstracted, descripted, represented, and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects, multi-level visual expressions, and spatial correlation analysis. The rationality of the method is verified through visualization scenarios of case information statistics for China, Henan cases, and cases related to Shulan. The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic, the discovery of the transmission law, and the spatial traceability of the cases. It has a good portability and good expansion performance, so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.

[1]  David S. Ebert,et al.  A pandemic influenza modeling and visualization tool☆ , 2011, Journal of Visual Languages & Computing.

[2]  Lawrence J Lau,et al.  The COVID-19 Epidemic in China , 2020 .

[3]  M. J. Kraak,et al.  Epidemics and pandemics in maps – the case of COVID-19 , 2020 .

[4]  A. Getis The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[5]  G. Matheron Principles of geostatistics , 1963 .

[6]  Wu Qunyong,et al.  A Survey of the Spatio-Temporal Data Model , 2016 .

[7]  Ping Ren,et al.  Preliminary study on exploring the trajectory of patients with COVID-19 by Data mining algorithms , 2020 .

[8]  May Yuan,et al.  GIS Representation for Visualizing and Mining Geographic Dynamics , 2003 .

[9]  Jin Chen,et al.  Combining Usability Techniques to Design Geovisualization Tools for Epidemiology , 2005, Cartography and geographic information science.

[10]  Pemetaan Jumlah Balita,et al.  Spatial Scan Statistic , 2014, Encyclopedia of Social Network Analysis and Mining.

[11]  L. Anselin Local Indicators of Spatial Association—LISA , 2010 .

[12]  Ding Zhang,et al.  Modelling the evolution trajectory of COVID-19 in Wuhan, China: experience and suggestions , 2020, Public Health.

[13]  R. Geary,et al.  The Contiguity Ratio and Statistical Mapping , 1954 .

[14]  K. Looker,et al.  Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework , 2020, BMJ Open.

[15]  P. Moran The Interpretation of Statistical Maps , 1948 .

[16]  C. Granell,et al.  Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain , 2011, International journal of health geographics.

[17]  Landon Fridman Detwiler,et al.  Visualization and analytics tools for infectious disease epidemiology: A systematic review , 2014, J. Biomed. Informatics.

[18]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[19]  Michael F. Worboys,et al.  A Unified Model for Spatial and Temporal Information , 1994, Comput. J..