ABSTRACT
After some initial hesitancy at the beginning of the COVID-19 pandemic, the academic community agreed that the infection process is mostly airborne and generally associated with closed environments. Therefore, assessing the indoor infection probability is mandatory to contain the spread of the disease, especially in those environments, like school classrooms, hospital wards or public transportation, with higher risk of overcrowding. For this reason, we developed a software tool in Python to compute infection probability and determine those mechanisms that contribute to reduce its diffusion in closed settings. In this paper, we will briefly illustrate the model we used and focus our attention on the description of the main features of the software and give some examples of how it can be used in clinical practice to predict the spread of the disease in the rooms of a generic ward, optimize room occupancy or drive healthcare workers activity schedule. Finally, some limitations and further implementations of our work will be reported.
ABSTRACT
Airborne transmission is one of the main routes of SARS-CoV-2 spread. It is important to determine the circumstances under which the risk of airborne transmission is increased as well as the effective strategy to reduce such risk. This study aimed to develop a modified version of the Wells-Riley model with indoor CO2 to estimate the probability of airborne transmission of SARS-CoV-2 Omicron strains with a CO2 monitor and to evaluate the validity of this model in actual clinical practices. We used the model in three suspected cases of airborne transmission presented to our hospital to confirm its validity. Next, we estimated the required indoor CO2 concentration at which R0 does not exceed 1 based on the model. The estimated R0 (R0, basic reproduction number) based on the model in each case were 3.19 in three out of five infected patients in an outpatient room, 2.00 in two out of three infected patients in the ward, and 0.191 in none of the five infected patients in another outpatient room. This indicated that our model can estimate R0 with an acceptable accuracy. In a typical outpatient setting, the required indoor CO2 concentration at which R0 does not exceed 1 is below 620 ppm with no mask, 1000 ppm with a surgical mask and 16000 ppm with an N95 mask. In a typical inpatient setting, on the other hand, the required indoor CO2 concentration is below 540 ppm with no mask, 770 ppm with a surgical mask, and 8200 ppm with an N95 mask. These findings facilitate the establishment of a strategy for preventing airborne transmission in hospitals. This study is unique in that it suggests the development of an airborne transmission model with indoor CO2 and application of the model to actual clinical practice. Organizations and individuals can efficiently recognize the risk of SARS-CoV-2 airborne transmission in a room and thus take preventive measures such as maintaining good ventilation, wearing masks, or shortening the exposure time to an infected individual by simply using a CO2 monitor.
Subject(s)
Air Pollution, Indoor , COVID-19 , Humans , SARS-CoV-2 , Carbon Dioxide , Masks , ProbabilityABSTRACT
The current COVID-19 pandemic has highlighted the importance of health safety assessment in various indoor scenarios. Computational fluid dynamics (CFD) combined with a modified Wells-Riley equation provides a powerful tool to analyse local infection probability in an indoor space. Compared to a single infection probability characterising the space in the traditional Wells-Riley model, the coupled approach provides a distribution of infection probability within the space. Furthermore, this approach avoids assuming a well-mixed state, usually related to Wells-Riley equation. This study compares displacement and mixing ventilation strategies with four different ventilation rates to assess the local quanta concentrations modelled using passive scalar transport approach. The simulation results are processed to also account for the effect of wearing masks and vaccinations. The result show that a well-designed displacement ventilation system can significantly reduce infection probability compared to mixing ventilation system at similar airflow rate. Additionally, the results emphasised the importance of wearing mask and getting vaccinated as a means of reducing infection probability. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
This study experimentally measured the ventilation efficiency in road vehicles. Two air circulation methods, air conditioning and opening windows, were considered and their ventilation efficiencies were measured for a sedan and a cutaway bus. The ventilation efficiencies have been evaluated by measuring the aerosol concentration parameter at different locations inside the vehicle. For both vehicles, any of the ventilation scenarios significantly increased the air exchange rate. The best performance was shown when all windows were open in a moving vehicle. As an illustration of using the obtained measurements, respiratory infection probability was calculated using the Wells-Riley model. Any of the ventilation cases significantly decreased the infection risk. The ventilation efficiency and infection probability were highly dependent on the air circulation method and vehicle type. © 2023 The Author(s)
ABSTRACT
The possibility of unfavorable leakages, especially with infectious diseases, in heat recovery systems in air handling units (AHU) is an essential issue. Typical configurations of AHU are analyzed in this aspect, based on their pressure distribution. It is shown that analyzing only for the design conditions is insufficient and that the changing pressure drops of the air filters due to their nonuniform soiling should be taken into account. The novelty of this paper is in proposed method of considering these leaks in the Wells-Riley model, widely used in the literature for airborne transmission of infectious diseases, including the leakage correction factor fhrleak (outdoor fresh air correction factor) based on EATR (exhaust air transfer ratio). Using the proposed method, for typical rooms, on the example of the SARS-CoV-2 virus and its Delta and Omicron variants, it is shown that considering leaks in heat recovery systems in AHU increases the probability of pathogen transmission. The highest increase in the absolute value of the probability of infection is observed in the single office scenario (4.1%) and in the auditorium with a sick speaker scenario (2.7%). The highest increase in reproduction number is observed in the auditorium with a sick speaker scenario (2.69). Such significant changes in reproduction number, including its change from R < 1.0 to R > 1.0 (auditorium with sick speaker for Delta variant of the virus), are crucial from the point of view of considering event scenarios;they slow down or accelerate the pandemic. © 2023 Elsevier Ltd
ABSTRACT
In this paper we develop an approach to modeling and simulating the process of infection transmission among individuals and the effectiveness of protective counter-measures. We base our approach on pedestrian dynamics and we implement it as an extension of the Vadere simulation framework. In order to enable a convenient simulation process for a variety of scenarios, we allow the user to interact with the simulated virtual environment (VE) during run time, for example, by dynamically opening/closing doors for room ventilation and moving/stopping agents for re-positioning their locations. We calibrate and evaluate our approach on a real-life case study—simulating COVID-19 infection transmission in two kinds of scenarios: large-scale (such as the city of Münster, Germany) and small-scale (such as the most common indoor environments—classrooms, restaurants, etc.). By using the tunable parameters of our modeling approach, we can simulate and predict the effectiveness of specific anti-COVID protective measures, such as social distancing, wearing masks, self-isolation, schools closing, etc. [ FROM AUTHOR] Copyright of Simulation is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Ventilation systems for aircraft cabins are mainly used to maintain a comfortable environment in the cabin and ensure the health of passengers. This study evaluates the decontamination performance of two cabin ventilation systems, the displacement ventilation (DV) system and the mixing ventilation (MV) system, in preventing contamination by virus (COVID-19)-laden droplets. The Euler-Lagrange method was used to computationally model droplet dispersion of different diameters and their behavior in the two systems was contrastively analyzed. Statistics on droplet suspension ratios and duration as well as the infection probability of each passenger were also computed. It was found that11.07% fewer droplet remained suspended in the DV system were than those in the MV system 10s from droplet release. In addition, the number of droplets extracted from the exhausts in the DV system was 13.15% more than the MV system at the 400s mark. In the DV system, higher ambient wind velocities were also found to locally increase infection probability for passengers in certain locations.
ABSTRACT
To mitigate the spread of novel coronavirus, how to optimise COVID-19 medical waste location-transport strategies remains an open but urgent issue. In this paper, a novel digital twin-driven conceptual framework is proposed to improve the strategic decision on the location of temporary disposal centres and, subsequently, the operational decision on the transport of COVID-19 medical waste in the presence of hierarchical relationships amongst stakeholders, circular economy, uncertainty in infection probability, and service level. The circular economy aspect is measured by the reduction of infection risks and costs, as well as limiting exhaust emissions. The polyhedral uncertainty set is introduced to characterise stochastic infection probability. Digital twin technology is further used to estimate the upper and lower bound of the uncertainty set. Such a problem is formulated as a digital twin-driven robust bi-level mixed-integer programming model to minimise total infection risks on the upper level and total costs on the lower level. A hybrid solution strategy is designed to combine dual theory, Karush-Kuhn-Tucker (KKT) conditions, and a branch-and-bound approach. Finally, a real case study from Maharashtra in India is presented to evaluate the proposed model. Results demonstrate that the solution strategy performs well for such a complex problem because the CPU time required to conduct all experiments is less than one hour. Under a given uncertainty level of 36 and perturbation ratio of 20%, a regional transport strategy is preferred from generation points to transfer points, while a cross-regional one is usually implemented from transfer points to disposal centres. It is of significance to determine the bound of available temporary disposal centres. Using digital technology (e.g., digital twin) to accurately estimate the amount of COVID-19 medical waste is beneficial for controlling the pandemic. Reducing infection risks relative to cost is the prioritised goal in cleaning up COVID-19 medical waste within a relatively long period.
ABSTRACT
The possibility of unfavorable leakages, especially with infectious diseases, in heat recovery systems in air handling units (AHU) is an essential issue. Typical configurations of AHU are analyzed in this aspect, based on their pressure distribution. It is shown that analyzing only for the design conditions is insufficient and that the changing pressure drops of the air filters due to their nonuniform soiling should be taken into account. The novelty of this paper is in proposed method of considering these leaks in the Wells-Riley model, widely used in the literature for airborne transmission of infectious diseases, including the leakage correction factor fhrleak (outdoor fresh air correction factor) based on EATR (exhaust air transfer ratio). Using the proposed method, for typical rooms, on the example of the SARS-CoV-2 virus and its Delta and Omicron variants, it is shown that considering leaks in heat recovery systems in AHU increases the probability of pathogen transmission. The highest increase in the absolute value of the probability of infection is observed in the single office scenario (4.1%) and in the auditorium with a sick speaker scenario (2.7%). The highest increase in reproduction number is observed in the auditorium with a sick speaker scenario (2.69). Such significant changes in reproduction number, including its change from R < 1.0 to R > 1.0 (auditorium with sick speaker for Delta variant of the virus), are crucial from the point of view of considering event scenarios;they slow down or accelerate the pandemic.
ABSTRACT
A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.
ABSTRACT
COVID-19 has highlighted the need for indoor risk-reduction strategies. Our aim is to provide information about the virus dispersion and attempts to reduce the infection risk. Indoor transmission was studied simulating a dining situation in a restaurant. Aerosolized Phi6 viruses were detected with several methods. The aerosol dispersion was modeled by using the Large-Eddy Simulation (LES) technique. Three risk-reduction strategies were studied: (1) augmenting ventilation with air purifiers, (2) spatial partitioning with dividers, and (3) combination of 1 and 2. In all simulations infectious viruses were detected throughout the space proving the existence long-distance aerosol transmission indoors. Experimental cumulative virus numbers and LES dispersion results were qualitatively similar. The LES results were further utilized to derive the evolution of infection probability. Air purifiers augmenting the effective ventilation rate by 65% reduced the spatially averaged infection probability by 30%-32%. This relative reduction manifests with approximately 15 min lag as aerosol dispersion only gradually reaches the purifier units. Both viral findings and LES results confirm that spatial partitioning has a negligible effect on the mean infection-probability indoors, but may affect the local levels adversely. Exploitation of high-resolution LES jointly with microbiological measurements enables an informative interpretation of the experimental results and facilitates a more complete risk assessment.
Subject(s)
Air Pollution, Indoor , COVID-19 , Humans , SARS-CoV-2 , Restaurants , Air Pollution, Indoor/analysis , Respiratory Aerosols and DropletsABSTRACT
Due to the increasing amount of time that people are spending indoors, the need to ensure adequate ventilation has become a priority. The confirmed airborne transmission of COVID-19 highlights the necessity to consider the effect of ventilation on the reduction of the infection risk. In naturally ventilated buildings, the ventilation rate is not easy to determine, and it is difficult to estimate the risk to implement preventive measures. This paper presents a method to estimate the infection probability from CO2 concentration monitoring, which was applied to university classrooms. The effects of people's activity, classroom characteristics, occupancy and protective masks were also investigated. From the method, it is possible to calculate the infection probability using CO2 dataloggers that can be adopted as 'alarm' systems to keep the infection probability below a critical value. The method will enhance healthy conditions indoors and reduce the risk of infectious diseases in the future.
ABSTRACT
The building energy performance has been highly studied in the last decades considering the indoor environmental quality, and sustainability indicators to examine energy-efficient cost-optimal, and nZEB building levels for different building typologies. However, since the start of the COVID-19 pandemic in 2020, the usage and operation of public spaces and buildings have evolved according to COVID-19 measures produced by the authorities. Social distancing measures and HVAC system measures affecting energy performance and indoor environmental quality of the public buildings are consequently necessary for building energy performance studies. Thereby, it is aimed at re-considering an energy-efficient cost-optimal retrofitting approach for a primary school building case, under the COVID-19 measures to recast an energy-efficient cost-optimal level and apply a cost-efficiency criterion to search for the measures adapted nZEB scenarios. COVID-19 measures affecting building energy performance, such as social distancing and IEQ requirements, were analysed. Then, probable ventilation rates were controlled by the infection probability method to satisfy the limit number for infection. Thus, pre-COVID-19 and post-COVID-19 variables regarding occupancy density and HVAC operation were determined for the calculation process. Besides, retrofit scenarios were shaped to improve optic and thermophysical properties of the façade, lighting, and HVAC systems performance. Then, energy, LCC, thermal comfort and IEQ performance of retrofit scenarios were calculated with a calibrated model. Results were evaluated by applying the cost-efficiency criterion to find out nZEB scenarios. It can be stated that distinct LCC and energy use increments occurred in the cost-optimal range and nZEB level.
ABSTRACT
Worldwide pandemic by COVID-19 still continues. Ventilation is recommended as one of the important measures against virus infection. However, if ventilation measures are taken in the wrong way, not only the measures can’t reduce the risk of infection, but also they may make increase it. The effect of ventilation as an infection control, how to determine the volume flow rate requirement, and the ventilation method for effective air exchange has been studied. Based on these results, how we should consider effective ventilation to reduce the risk of viral infections were discussed in this paper. © 2021 The authors.
ABSTRACT
This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet dispersion for calculating the probability of SARS-CoV-2 infection in real life scenarios. The software application, referred to as TU Delft COVID-app, includes the modelling of human expiratory activities, unsteady and turbulent convection, droplet evaporation and thermal coupling. Data describing human expiratory activities have been obtained from selected studies involving measurements of the expelled droplets and the air flow during coughing, sneezing and breathing. Particle Image Velocimetry (PIV) measurements of the transient air flow expelled by a person while reciting a speech have been conducted with and without a surgical mask. The instantaneous velocity fields from PIV are used to determine the velocity flow rates used in the numerical simulations, while the average velocity fields are used for validation. Furthermore, the effect of surgical masks and N95 respirators on particle filtration and the probability of SARS-CoV-2 infection from a dose-response model have also been implemented in the application. Finally, the work includes a case-study of SARS-CoV-2 infection risk analysis during a conversation across a dining/meeting table that demonstrates the capability of the newly developed application.
Subject(s)
COVID-19 , Mobile Applications , Humans , Hydrodynamics , Masks , Risk Assessment , SARS-CoV-2ABSTRACT
Portable air cleaners (PACs) can remove airborne SARS-CoV-2 exhaled by COVID-19 infectors indoor. However, effectively locating PAC to reduce the infection risk is still poorly understood. Here, we propose a simple model by regressing an equation of seven similarity criteria based on CFD-modeled results of a scenario matrix of 128 cases for office rooms. The model can calculate the mean droplet nucleus concentration with very low computing costs. Combining this model with the Wells-Riley equation, we estimate the airborne infection risk when a PAC is located in different positions. The two similarity criteria, B p + and G p + , are critical for characterizing the effect of the position and airflow rate of PAC on the infection risk. An infection probability of less than 10% requires B p + to be larger than 144 and G p + to be larger than 0.001. These criteria imply that locating PAC in the center of the room is optimal under the premise that the airflow rate of PAC is greater than a certain level. The model provides an easy-to-use approach for real-time risk control strategy decisions. Furthermore, the placement strategies offer timely guidelines for precautions against the prolonged COVID-19 pandemic and common infectious respiratory diseases.
ABSTRACT
Coupled Wells-Riley (WR) and Computational Fluid Dynamics (CFD) modelling (WR-CFD) facilitates a detailed analysis of COVID-19 infection probability (IP). This approach overcomes issues associated with the WR 'well-mixed' assumption. The WR-CFD model, which makes uses of a scalar approach to simulate quanta dispersal, is applied to Chinese long-distance trains (G-train). Predicted IPs, at multiple locations, are validated using statistically derived (SD) IPs from reported infections on G-trains. This is the first known attempt to validate a coupled WR-CFD approach using reported COVID-19 infections derived from the rail environment. There is reasonable agreement between trends in predicted and SD IPs, with the maximum SD IP being 10.3% while maximum predicted IP was 14.8%. Additionally, predicted locations of highest and lowest IP, agree with those identified in the statistical analysis. Furthermore, the study demonstrates that the distribution of infectious aerosols is non-uniform and dependent on the nature of the ventilation. This suggests that modelling techniques neglecting these differences are inappropriate for assessing mitigation measures such as physical distancing. A range of mitigation strategies were analysed; the most effective being the majority (90%) of passengers correctly wearing high efficiency masks (e.g. N95). Compared to the base case (40% of passengers wearing low efficiency masks) there was a 95% reduction in average IP. Surprisingly, HEPA filtration was only effective for passengers distant from an index patient, having almost no effect for those in close proximity. Finally, as the approach is based on CFD it can be applied to a range of other indoor environments.
ABSTRACT
As suggested by many guidelines, a high ventilation rate is required to dilute the indoor virus particles and reduce the airborne transmission risk, i.e., dilution ventilation (DV). However, high ventilation rates may result in high energy costs. Ventilative cooling (VC), which requires high ventilation rates like DV, is an option to reduce the cooling energy consumption. By combining DV and VC, this paper investigated the operation of the mechanical ventilation system in high-rise buildings during the COVID-19 pandemic, aiming to minimizing the cooling related energy consumption and reducing COVID-19 transmission. First, a modified Wells-Riley model was proposed to calculate DV rates. The ventilation rate required to achieve VC was also introduced. Then, a new ventilation control strategy was proposed for achieving DV and VC. Finally, a case study was conducted on a real high-rise building, where the required DV rate and the impact of the settings of the mechanical ventilation on the energy savings were evaluated. The results indicate that the required ventilation rates vary from 36 m3/s to 3306 m3/s depending on the protective measures. When the occupants follow the protective measures, the proper settings of the mechanical ventilation system can reduce energy consumption by around 40%.
ABSTRACT
BACKGROUND: Classic compartmental models such as the susceptible-exposed-infectious-removed (SEIR) model all have the weakness of assuming a homogenous population, where everyone has an equal chance of getting infected and dying. Since it was identified in Hubei, China, in December 2019, COVID-19 has rapidly spread around the world and been declared a pandemic. Based on data from Hubei, infection and death distributions vary with age. To control the spread of the disease, various preventive and control measures such as community quarantine and social distancing have been widely used. OBJECTIVE: Our aim is to develop a model where age is a factor, considering the study area's age stratification. Additionally, we want to account for the effects of quarantine on the SEIR model. METHODS: We use the age-stratified COVID-19 infection and death distributions from Hubei, China (more than 44,672 infections as of February 11, 2020) as an estimate or proxy for a study area's infection and mortality probabilities for each age group. We then apply these probabilities to the actual age-stratified population of Quezon City, Philippines, to predict infectious individuals and deaths at peak. Testing with different countries shows the predicted number of infectious individuals skewing with the country's median age and age stratification, as expected. We added a Q parameter to the SEIR model to include the effects of quarantine (Q-SEIR). RESULTS: The projections from the age-stratified probabilities give much lower predicted incidences of infection than the Q-SEIR model. As expected, quarantine tends to delay the peaks for both the exposed and infectious groups, and to "flatten" the curve or lower the predicted values for each compartment. These two estimates were used as a range to inform the local government's planning and response to the COVID-19 threat. CONCLUSIONS: Age stratification combined with a quarantine-modified model has good qualitative agreement with observations on infections and death rates. That younger populations will have lower death rates due to COVID-19 is a fair expectation for a disease where most fatalities are among older adults.
Subject(s)
COVID-19/epidemiology , Models, Statistical , Adult , Age Factors , COVID-19/diagnosis , COVID-19/transmission , China/epidemiology , Female , Humans , Male , Needs Assessment , Probability , Quarantine , SARS-CoV-2/isolation & purificationABSTRACT
The World Health Organization (WHO) announced that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may spread through aerosols, so-called airborne transmission, especially in a poorly ventilated indoor environment. Ventilation protects the occupants against airborne transmission. Various studies have been performed on the importance of sufficient ventilation for diluting the concentration of virus and lowering any subsequent dose inhaled by the occupants. However, the ventilation situation can be problematic in public buildings and other shared spaces, such as shops, offices, schools, and restaurants. If ventilation is provided by opening windows, the outdoor airflow rate depends strongly on the specific local conditions (opening sizes, relative positions, climatic and weather conditions). This study uses field measurements to analyze the natural ventilation performance in a school building according to the window opening rates, positions, and weather conditions. The ventilation rates were calculated by the tracer gas decay method, and the infection risk was assessed using the Wells-Riley equation. Under cross-ventilation conditions, the average ventilation rates were measured at 6.51 h-1 for 15% window opening, and 11.20 h-1 for 30% window opening. For single-sided ventilation, the ventilation rates were reduced to about 30% of the values from the cross-ventilation cases. The infection probability is less than 1% in all cases when a mask is worn and more than 15% of the windows are open with cross-ventilation. With single-sided ventilation, if the exposure time is less than 1 h, the infection probability can be kept less than 1% with a mask. However, the infection probability exceeds 1% in all cases where exposure time is greater than 2 h, regardless of whether or not a mask is worn. Also, when the air conditioner was operated with a window opening ratio of 15%, power consumption increased by 10.2%.