Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings
A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2459 |
| _version_ | 1797614366201217024 |
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| author | Jelle Vanhaeverbeke Emiel Deprost Pieter Bonte Matthias Strobbe Jelle Nelis Bruno Volckaert Femke Ongenae Steven Verstockt Sofie Van Hoecke |
| author_facet | Jelle Vanhaeverbeke Emiel Deprost Pieter Bonte Matthias Strobbe Jelle Nelis Bruno Volckaert Femke Ongenae Steven Verstockt Sofie Van Hoecke |
| author_sort | Jelle Vanhaeverbeke |
| collection | DOAJ |
| description | A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO<sub>2</sub>) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment. |
| first_indexed | 2024-03-11T07:11:24Z |
| format | Article |
| id | doaj.art-667cd4b59aea4669bec8a0b3fd5f8948 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:11:24Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-667cd4b59aea4669bec8a0b3fd5f89482023-11-17T08:35:04ZengMDPI AGSensors1424-82202023-02-01235245910.3390/s23052459Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office BuildingsJelle Vanhaeverbeke0Emiel Deprost1Pieter Bonte2Matthias Strobbe3Jelle Nelis4Bruno Volckaert5Femke Ongenae6Steven Verstockt7Sofie Van Hoecke8IDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumIDLab, Ghent University-imec, 9052 Ghent, BelgiumA healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO<sub>2</sub>) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment.https://www.mdpi.com/1424-8220/23/5/2459COVID-19 aerosol transmission risk estimationdynamic dashboardstreaming architectureindoor sensorsIoTsemantic data |
| spellingShingle | Jelle Vanhaeverbeke Emiel Deprost Pieter Bonte Matthias Strobbe Jelle Nelis Bruno Volckaert Femke Ongenae Steven Verstockt Sofie Van Hoecke Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings Sensors COVID-19 aerosol transmission risk estimation dynamic dashboard streaming architecture indoor sensors IoT semantic data |
| title | Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings |
| title_full | Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings |
| title_fullStr | Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings |
| title_full_unstemmed | Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings |
| title_short | Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings |
| title_sort | real time estimation and monitoring of covid 19 aerosol transmission risk in office buildings |
| topic | COVID-19 aerosol transmission risk estimation dynamic dashboard streaming architecture indoor sensors IoT semantic data |
| url | https://www.mdpi.com/1424-8220/23/5/2459 |
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