Jung J, et al. Infect Chemother. 2021 Jun;53(2):332-341. https://doi.org/10.3947/ic.2021.0046

In this study, the results of an airflow investigation conducted on 7 June 2015 as part of a series of epidemiologic investigations at Pyeongtaek St. Mary’s Hospital, South Korea, were investigated. The study involved 38 individuals who were infected directly and indirectly with Middle East Respiratory Syndrome (MERS), by a super-spreader patient. Tracer gas experiments conducted on the eighth floor, where the initial patient was hospitalized, confirmed that the tracer gas spread to adjacent patient rooms and rooms across corridors. In particular, the experiment with an external wind direction and speed similar to those during the hospitalization of the initial patient revealed that the air change rate was 17–20 air changes per hour (ACH), with air introduced through the window in the room of the infected patient (room 8104). The tracer gas concentration of room 8110, which was the farthest room, was 7.56% of room 8104, indicating that a high concentration of gas has spread from room 8104 to rooms across the corridor. In contrast, the tracer gas was barely detected in a maternity ward to the south of room 8104, where there was no secondary infected patient. Moreover, MERS is known to spread mainly by droplets through close contact, but long-distance dispersion is probable in certain environments, such as that of a super-spreader patient hospitalized in a room without ventilation, hospitals with a central corridor type, and indoor airflow dispersion due to external wind.

Middle East respiratory syndrome (MERS) is known to be transmitted through close contact. However, epidemiological surveys of MERS in Korea indicated that some secondary patients were infected without close contact. Therefore, the possibility of other transmission routes must be identified. In this study, the possibility of MERS spreading through airflow was investigated on the eighth floor of Pyeongtaek St Mary's Hospital. Computational fluid dynamics was used to analyse the indoor airflow and passive tracer diffusion during the index patient's stay. Six cases were simulated for different outdoor wind directions and indoor mechanical ventilation operations. When a passive tracer was released in ward 8104, where the index patient was hospitalized, the passive tracer spread through the indoor airflow, which was created by the outdoor airflow. Ward 8109, which had the largest number of infected cases and was far distant from ward 8104, showed passive tracer concentration in all cases. This result indicates that MERS may have spread through airflow. The study results do not imply that the infection pathway of MERS is airborne. However, the results show the possibility of MERS spreading through airflow in specific environments such as poor ventilation environments.

Indoor air quality (IAQ) plays a crucial role in safeguarding the health of both patients and healthcare workers in hospital environments. Accurate IAQ analysis and prediction are vital for optimizing ventilation, filtration, and other control measures to maintain a safe indoor atmosphere. This study investigates IAQ in hospital spaces by utilizing long‐term data from internet of things (IoT) sensors installed in general wards and negative pressure isolation wards. Given the significant influence of outdoor air, IAQ requires continuous monitoring across different seasons and extended periods. In this study, IAQ was measured over nearly a year, capturing seasonal variations and long‐term trends. Clustering algorithms were applied to identify complex patterns and detect anomalies in key IAQ parameters, including temperature, CO ₂ concentration, and particulate matter 2.5 μ m (PM _2.5 ). These clustering results were then integrated into a long short‐term memory (LSTM) model to enhance IAQ prediction for subsequent time steps. The findings indicate that incorporating clustering results as input variables substantially improves IAQ prediction accuracy. Notably, the root mean squared error for PM _2.5 prediction decreased from 8.51 to 3.99 when clustering results were included. This study underscores the potential of leveraging IoT sensors and machine learning techniques for real‐time IAQ monitoring and forecasting in hospital settings. These insights can support the development of effective control strategies to maintain a healthy and comfortable indoor environment for both patients and healthcare workers.

Identifying infection transmission routes in hospitals may prevent the spread of respiratory viruses and mass infections. Most previous related research focused on the air movement of passive tracers, which typically represent breathing. In this study, particle evaporation and dispersions with various particle sizes were applied to evaluate particle movement because of breathing and coughing using computational fluid dynamics (CFD) simulations. Pyeongtaek St. Mary Hospital, where a Middle East respiratory syndrome (MERS) index patient infected several patients on the same floor, was used for a case study. We compared the dispersion characteristics of various particle sizes and validated results by comparing infection rates in different ward. Results indicated that droplets spread across the corridor and dispersed to wards that were more than 17 m apart from the index patient by natural ventilation. Droplets from exhaled breath under steady-state simulation showed a wider range of dispersion than cough droplets under transient simulation, but cough droplet dispersion was more consistent with the actual infection rate in each ward. Cough droplets sized under 75 µm evaporated to 26% of the initial size and started to disperse into the corridor within one minute; in nine minutes, droplets dispersed throughout every ward. This study may increase awareness on the dispersion characteristics of infectious particles.