Aerial dispersion of human exhaled microbial contaminants and subsequent contamination of surfaces is a potential route for infection transmission in hospitals. Most general hospital wards have ventilation systems that drive air and thus contaminants from the patient areas towards the corridors. This study investigates the transport mechanism and deposition patterns of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) within a typical six bedded general inpatient ward cubicle through numerical simulation. It demonstrates that both air change and exhaust airflow rates have significant effects on not only the airflow but also the particle distribution within a mechanically ventilated space. Moreover, the location of an infected patient within the ward cubicle is crucial in determining the extent of infection risk to other ward occupants. Hence, it is recommended to provide exhaust grilles in close proximity to a patient, preferably above each patient’s bed. To achieve infection prevention and control, high exhaust airflow rate is also suggested. Regardless of the ventilation design, all patients and any surfaces within a ward cubicle should be regularly and thoroughly cleaned and disinfected to remove microbial contamination. The outcome of this study can serve as a source of reference for hospital management to better ventilation design strategies for mitigating the risk of infection.

The residential sector accounts for a significant and increasing portion of the national energy use. Cooling energy reduction in the public housing sector is one of the key success factors for sustainable building development measures especially in the sub-tropics. This study proposes a hybrid EnergyPlus (EP) - artificial neural network (ANN) model, which is more flexible and time- efficient than the conventional cooling energy simulation methods, for simulating the cooling energy consumption in the Hong Kong public housing sector and evaluates the cooling energy impacts related to building materials, window sizes, indoor-outdoor temperature variations and apartment sizes. The results show that climate changes and temperature set-points have the greatest impact on cooling energy use (-19.9% to 24.1%), followed by flat size combinations (-13.4% to 27.9%). The proposed model can be a useful tool for policymakers to establish sustainable public housing development plans in Hong Kong.