COVID-19 Contagion Risk Estimation Model for Indoor Environments
COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/19/7668 |
| _version_ | 1797476828008415232 |
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| author | Sandra Costanzo Alexandra Flores |
| author_facet | Sandra Costanzo Alexandra Flores |
| author_sort | Sandra Costanzo |
| collection | DOAJ |
| description | COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells–Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO<sub>2</sub> concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population. |
| first_indexed | 2024-03-09T21:09:16Z |
| format | Article |
| id | doaj.art-83abd110ab09437f8fe9ac0d62b44070 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:09:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-83abd110ab09437f8fe9ac0d62b440702023-11-23T21:52:53ZengMDPI AGSensors1424-82202022-10-012219766810.3390/s22197668COVID-19 Contagion Risk Estimation Model for Indoor EnvironmentsSandra Costanzo0Alexandra Flores1DIMES, Università della Calabria, 87036 Rende, ItalyDIMES, Università della Calabria, 87036 Rende, ItalyCOVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells–Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO<sub>2</sub> concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.https://www.mdpi.com/1424-8220/22/19/7668COVID-19SARS-CoV-2smart healthcarecontagion-risk monitoringaerosol |
| spellingShingle | Sandra Costanzo Alexandra Flores COVID-19 Contagion Risk Estimation Model for Indoor Environments Sensors COVID-19 SARS-CoV-2 smart healthcare contagion-risk monitoring aerosol |
| title | COVID-19 Contagion Risk Estimation Model for Indoor Environments |
| title_full | COVID-19 Contagion Risk Estimation Model for Indoor Environments |
| title_fullStr | COVID-19 Contagion Risk Estimation Model for Indoor Environments |
| title_full_unstemmed | COVID-19 Contagion Risk Estimation Model for Indoor Environments |
| title_short | COVID-19 Contagion Risk Estimation Model for Indoor Environments |
| title_sort | covid 19 contagion risk estimation model for indoor environments |
| topic | COVID-19 SARS-CoV-2 smart healthcare contagion-risk monitoring aerosol |
| url | https://www.mdpi.com/1424-8220/22/19/7668 |
| work_keys_str_mv | AT sandracostanzo covid19contagionriskestimationmodelforindoorenvironments AT alexandraflores covid19contagionriskestimationmodelforindoorenvironments |