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

Abstract

Abstract The developed numerical model assesses the risk of a COVID-19 infection in a room based on the measurements of temperature, relative humidity, CO2 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.