JAUE2022-134: Analysis of the Airborne Viral Infections Distribution in an Outpatient Department Reflecting the Action Variables of Hospital Visitors

Abstract

Recently, cases of infection through heating, ventilating, and air conditioning (HVAC) systems in buildings are rising due to Coronavirus infection (COVID-19), which calls for attention to the spread of viral infections through indoor airflow. The preceding research used a machine learning (ML) algorithm to predict the number of viral infections according to the number of visitors. This research re-predicted the number of viral infections according to visitors' behavior by applying visitors' visit characteristics and action variables to the preceding research algorithm. Additionally, using the predicted number of viral infections, the number of visitors in the outpatient department was classified into the highest and lowest days of the week, and the airborne viral infections per room were analyzed by time frame. This research aims to serve as basic data for the HVAC control algorithm in each room of the outpatient department responding to the number of visitors and viral infections.