Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19

Abstract

In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this paper combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients' situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3) An efficient epidemic prevention and anti-epidemic method, which is of referential significance, is proposed. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients' relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups. INDEX TERMS COVID-19; geographic knowledge graph; spatiotemporal big data; visual analysis

User-centered visual explorer of in-process comparison in spatiotemporal space

“…This also directly defines the research basis for us. Jiang et al (2020) presented a knowledge graph system to interactively explore epidemic situations. Yang et al noticed that based on crowd movement and control measures may have an impact on the epidemic, so they proposed EpiMob (Yang et al 2022), a VA system which simulates the changes in human mobility and infection status.…”

Espaço E Covid-19:

“…KIM et al, 2021;SCARPONE et al, 2020;KWOK et al, 2021;BUJA et al, 2020;VAZ, 2021). Como exemplo, tem-se a pesquisa de Lenzen et al (2020) BHERWANI et al, 2021;ZHANG et al, 2020;SANGIORGIO;PARISI, 2020;GIANQUINTIERI et al, 2020;POURGHASEMI et al, 2020;SHAW et al, 2021;KARAIVANOV, 2020;PAPASTEFANOPOULOS;VICTOR OKHUESE, 2020;LINARDATOS;KOTSIANTIS, 2020;CHENG et al, 2020;CHINAZZI et al, 2020;KONICEK et al, 2020;JIANG et al, 2020;PANG et al, 2021;ZHOU et al, 2020).…”

Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran

“…These data are used to understand issues surrounding COVID-19 and to guide related decision-making. In [34] an analysis of the COVID-19 patients’ situations and their relationships is designed. Combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, this work is useful in community prediction, discovering patients’ relationships, the analysis of the spatiotemporal distribution of patients, and the prevention and control of high-risk groups.…”

“…One study found out that despite the stay-at-home orders issued by the organizations, the role of friends and families should be emphasized to promote compliance to the protocols [45] . As such relationships are mainly realized in social media, systematic and structural analysis of its content is very important [34] .…”