Numerical Model and System for Prediction and Reduction of Indoor Infection Risk

The developed numerical model assesses the risk of a COVID-19 infection in a room based on the measurements of temperature, relative humidity, CO 2 and particle concentration, as well as the number of people and occurrences of speech, coughing, and sneezing obtained through a low-cost sensor system....

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Bibliographic Details
Published in:Latvian Journal of Physics and Technical Sciences 2023-12, Vol.60 (s6), p.5-19
Main Authors: Virbulis, J., Telicko, J., Sabanskis, A., Vidulejs, D.D., Jakovics, A.
Format: Article
Language:English
Subjects:
Aerosols
Air purification
Building management systems
Carbon dioxide
COVID-19
Disease transmission
Heat
Humidity
Infections
Mathematical models
Numerical models
Relative humidity
Risk levels
Sedimentation & deposition
Sensors
Severe acute respiratory syndrome coronavirus 2
Sneezing
Ventilation
Viral infections
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Summary:The developed numerical model assesses the risk of a COVID-19 infection in a room based on the measurements of temperature, relative humidity, CO 2 and particle concentration, as well as the number of people and occurrences of speech, coughing, and sneezing obtained through a low-cost sensor system. As the model operates faster than real-time, it can dynamically inform the persons in the room or building management system about the predicted risk level. When the infection risk is high, the model can activate an air purifier equipped with filtration and UV-C disinfection. This solution improves energy efficiency by reducing the ventilation intensity required during colder seasons to maintain the same safety level and activating the purifier only when the predicted infection risk surpasses a specified threshold.
ISSN:2255-8896
0868-8257
2255-8896
2199-6156
DOI:10.2478/lpts-2023-0041