| Keywords |
Outpatient department; Air-conditioning control; Machine learning algorithm; Infectious bacteria; Behavioral variable |
| Abstract |
Recently, because of COVID-19 cases emerging due to the air conditioning system of buildings, the spread of infectious bacteria through indoor airflow is receiving continuous attention. Since the Outpatient Department maintains general air-conditioning operation conditions 20% outdoor-air and 80% recycled air, the exhaustion of infectious air currents is not fast enough at times when the number of visitors increases, which might lead to the nth infection. Due to this, in preceding research, the number of infectious bacteria depending on the number of visitors was predicted using ML algorithms. In this research, the characteristics and behavioral variables of visitors were applied to the algorithm used in the preceding research to predict once more the number of infectious bacteria based on the behavior of visitors. Also, the predicted number of infectious bacteria was used to analyze airborne infectious bacteria by room and by time of the day after categorizing the number of visitors into days of the week in which it is at its maximum, and days of the week in which it is at its minimum.
This research results are expected to be used as baseline data on air-conditioning system control algorithms that respond to changes in the number of visitors and infectious bacteria by room in the Outpatient Department. |