ABSTRACT
The efficient natural ventilation in indoor environments is extremely important especially this period with the appearance of new hazardous viruses such as COVID-19. It is well known that the maximum wind speed causes the lowest individual exposure to hazardous substances in an environment (either indoor or outdoor) and as a result its reliable prediction by a numerical model (either simple or complex) becomes of utmost importance. In this study a deterministic model, that was developed for the outdoor environment, is examined as a possible candidate to predict the maximum wind speed in indoor environments. For the needs of the study a wind tunnel experiment is simulated by the LES methodology in order to acquire the maximum wind speed at various locations in an indoor environment. Then the deterministic model, without any change in its parameters, is validated successfully with the LES maximum wind speeds. The present deterministic model can be incorporated in simple methodologies (e.g. RANS) provided that the latest are able to predict the mean speed, the turbulent intensity and a hydrodynamic time scale. © British Crown Copyright (2022)
ABSTRACT
The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals' exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton's Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.