A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML
With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed pla...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/1/2 |
| _version_ | 1797543864045666304 |
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| author | Soroush Ojagh Sara Saeedi Steve H. L. Liang |
| author_facet | Soroush Ojagh Sara Saeedi Steve H. L. Liang |
| author_sort | Soroush Ojagh |
| collection | DOAJ |
| description | With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users’ contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%. |
| first_indexed | 2024-03-10T13:52:29Z |
| format | Article |
| id | doaj.art-9e8558112cb142b18d50ce475ff01942 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T13:52:29Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-9e8558112cb142b18d50ce475ff019422023-11-21T02:02:17ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-12-01101210.3390/ijgi10010002A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGMLSoroush Ojagh0Sara Saeedi1Steve H. L. Liang2Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 4V8, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 4V8, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 4V8, CanadaWith the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users’ contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%.https://www.mdpi.com/2220-9964/10/1/2trajectory analysisgraph-based data modelOGC IndoorGML standardCOVID-19 contact tracing |
| spellingShingle | Soroush Ojagh Sara Saeedi Steve H. L. Liang A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML ISPRS International Journal of Geo-Information trajectory analysis graph-based data model OGC IndoorGML standard COVID-19 contact tracing |
| title | A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML |
| title_full | A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML |
| title_fullStr | A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML |
| title_full_unstemmed | A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML |
| title_short | A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML |
| title_sort | person to person and person to place covid 19 contact tracing system based on ogc indoorgml |
| topic | trajectory analysis graph-based data model OGC IndoorGML standard COVID-19 contact tracing |
| url | https://www.mdpi.com/2220-9964/10/1/2 |
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