Window-Windcatcher for Enhanced Thermal Comfort, Natural Ventilation and Reduced COVID-19 Transmission

We investigate and test the effectiveness of a novel window windcatcher device (WWC), as a means of improving natural ventilation in buildings. Using ANSYS CFX, the performance of the window-windcatcher is compared to a control case (no window-windcatcher), in three different geographic locations (Cardiff, Doha and Amman) which are representative of three different types of atmospheric conditions. The proposed window-windcatcher has been shown to improve both thermal comfort and indoor air quality by increasing the actual-to-required ventilation ratio by up to 9% compared to the control case as per the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards. In addition, the locations with minimum velocities have been identified. Those locations correspond to the regions with a lower infection risk of spreading airborne viruses such as SARS-CoV-2, which is responsible for the COVID-19 pandemic.

Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model.

We develop a spatially dependent generalization to the Wells-Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection-diffusion-reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies-a courtroom, a restaurant, and a hospital ward-demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws. The model can be easily updated for different parameter values and can be used to make predictions on the expected time taken to become infected, for any location, emission rate, and ventilation level. The results have direct applicability in mitigating the spread of the COVID-19 pandemic.

A General Computational Framework for COVID-19 Modelling with Applications to Testing Varied Interventions in Education Environments

We construct a spatially-compartmental, individual-based model of the spread of SARS-CoV-2 in indoor spaces. The model can be used to predict the infection rates in a variety of locations when various non-pharmaceutical interventions (NPIs) are introduced. Tasked by the Welsh Government, we apply the model to secondary schools and to Further and Higher Education environments. Specifically, we consider student populations mixing in a classroom and in halls of residence. We focus on assessing the potential efficacy of Lateral Flow Devices (LFDs) when used in broad-based screens for asymptomatic infection or in 'test-to-release’scenarios in which individuals who have been exposed to infection are released from isolation after a negative LFD result. LFDs are also compared to other NPIs;we find that, although LFD testing can be used to mitigate the spread of SARS-CoV-2, it is more effective to invest in personal protective equipment, e.g., masks, and in increasing ventilation quality. In addition, we provide an open-access and user-friendly online applet that simulates the model, complete with user tutorials to encourage the use of the model to aid educational policy decisions as input infection data becomes available.