ABSTRACT
Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and to envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters limitations due to the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets' initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. There is a noticeable inconsistency around these collected data amongst many reported studies. This study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Therefore, this paper briefly reviews the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. The relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures are also reviewed. Eventually, the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis are scurtinized to pave a pathway toward future studies ensuing these identified gaps and limitations.
ABSTRACT
Airborne transmission is an important route of spread of viral diseases (e.g., COVID-19) inside the confined spaces. In this respect, computational fluid dynamics (CFD) emerged as a reliable and fast tool to understand the complex flow patterns in such spaces. Most of the recent studies, nonetheless, focused on the spatial distribution of airborne pathogens to identify the infection probability without considering the exposure time. This research proposes a framework to evaluate the infection probability related to both spatial and temporal parameters. A validated Eulerian-Lagrangian CFD model of exhaled droplets is first developed and then evaluated with an office case study impacted by different ventilation strategies (i.e., cross- (CV), single- (SV), mechanical- (MV) and no-ventilation (NV)). CFD results were analyzed in a bespoke code to calculate the tempo-spatial distribution of accumulated airborne pathogens. Furthermore, two indices of local and general infection risks were used to evaluate the infection probability of the ventilation scenarios. The results suggest that SV has the highest infection probability while SV and NO result in higher dispersions of airborne pathogens inside the room. Eventually, the time history of indices reveals that the efficiency of CV and MV can be poor in certain regions of the room.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the rapidly spreading pandemic COVID-19 in the world. As an effective therapeutic strategy is not introduced yet and the rapid genetic variations in the virus, there is an emerging necessity to design, evaluate and apply effective new vaccines. An acceptable vaccine must elicit both humoral and cellular immune responses, must have the least side effects and the storage and transport systems should be available and affordable for all countries. These vaccines can be classified into different types: inactivated vaccines, live-attenuated virus vaccines, subunit vaccines, virus-like particles (VLPs), nucleic acid-based vaccines (DNA and RNA) and recombinant vector-based vaccines (replicating and non-replicating viral vector). According to the latest update of the WHO report on April 2nd, 2021, at least 85 vaccine candidates were being studied in clinical trial phases and 184 candidate vaccines were being evaluated in pre-clinical stages. In addition, studies have shown that other vaccines, including the Bacillus Calmette-Guérin (BCG) vaccine and the Plant-derived vaccine, may play a role in controlling pandemic COVID-19. Herein, we reviewed the different types of COVID-19 candidate vaccines that are currently being evaluated in preclinical and clinical trial phases along with advantages, disadvantages or adverse reactions, if any.