New dose-response model and SARS-CoV-2 quanta emission rates for calculating the long-range airborne infection risk.

Predictive models for airborne infection risk have been extensively used during the pandemic, but there is yet still no consensus on a common approach, which may create misinterpretation of results among public health experts and engineers designing building ventilation. In this study we applied the latest data on viral load, aerosol droplet sizes and removal mechanisms to improve the Wells Riley model by introducing the following novelties i) a new model to calculate the total volume of respiratory fluid exhaled per unit time ii) developing a novel viral dose-based generation rate model for dehydrated droplets after expiration iii) deriving a novel quanta-RNA relationship for various strains of SARS-CoV-2 iv) proposing a method to account for the incomplete mixing conditions. These new approaches considerably changed previous estimates and allowed to determine more accurate average quanta emission rates including omicron variant. These quanta values for the original strain of 0.13 and 3.8 quanta/h for breathing and speaking and the virus variant multipliers may be used for simple hand calculations of probability of infection or with developed model operating with six size ranges of aerosol droplets to calculate the effect of ventilation and other removal mechanisms. The model developed is made available as an open-source tool.

Reducing the risk of viral contamination during the coronavirus pandemic by using a protective curtain in the operating room.

BACKGROUND: Airborne transmission diseases can transfer long and short distances via sneezing, coughing, and breathing. These airborne repertory particles can convert to aerosol particles and travel with airflow. During the Coronavirus disease 2019 (COVID-19) pandemic, many surgeries have been delayed, increasing the demand for establishing a clean environment for both patient and surgical team in the operating room. METHODS: This study aims to investigate the hypothesis of implementing a protective curtain to reduce the transmission of infectious contamination in the surgical microenvironment of an operating room. In this regard, the spread of an airborne transmission disease from the patient was evaluated, consequently, the exposure level of the surgical team. In the first part of this study, a mock surgical experiment was established in the operating room of an academic medical center in Norway. In the second part, the computational fluid dynamic technique was performed to investigate the spread of airborne infectious diseases. Furthermore, the field measurement was used to validate the numerical model and guarantee the accuracy of the applied numerical models. RESULTS: The results showed that the airborne infectious agents reached the breathing zone of the surgeons. However, using a protective curtain to separate the microenvironment between the head and lower body of the patient resulted in a 75% reduction in the spread of the virus to the breathing zone of the surgeons. The experimental results showed a surface temperature of 40 ˚C, which was about a 20 ˚C increase in temperature, at the wound area using a high intensity of the LED surgical lamps. Consequently, this temperature increase can raise the patient's thermal injury risk. CONCLUSION: The novel method of using a protective curtain can increase the safety of the surgical team during the surgery with a COVID-19 patient in the operating room.

Zonal modeling of air distribution impact on the long-range airborne transmission risk of SARS-CoV-2.

A widely used analytical model to quantitatively assess airborne infection risk is the Wells-Riley model which is limited to complete air mixing in a single zone. However, this assumption tends not to be feasible (or reality) for many situations. This study aimed to extend the Wells-Riley model so that the infection risk can be calculated in spaces where complete mixing is not present. Some more advanced ventilation concepts create either two horizontally divided air zones in spaces as displacement ventilation or the space may be divided into two vertical zones by downward plane jet as in protective-zone ventilation systems. This is done by evaluating the time-dependent distribution of infectious quanta in each zone and by solving the coupled system of differential equations based on the zonal quanta concentrations. This model introduces a novel approach by estimating the interzonal mixing factor based on previous experimental data for three types of ventilation systems: incomplete mixing ventilation, displacement ventilation, and protective zone ventilation. The modeling approach is applied to a room with one infected and one susceptible person present. The results show that using the Wells-Riley model based on the assumption of completely air mixing may considerably overestimate or underestimate the long-range airborne infection risk in rooms where air distribution is different than complete mixing, such as displacement ventilation, protected zone ventilation, warm air supplied from the ceiling, etc. Therefore, in spaces with non-uniform air distribution, a zonal modeling approach should be preferred in analytical models compared to the conventional single-zone Wells-Riley models when assessing long-range airborne transmission risk of infectious respiratory diseases.

Modeling the impact of indoor relative humidity on the infection risk of five respiratory airborne viruses.

With a modified version of the Wells-Riley model, we simulated the size distribution and dynamics of five airborne viruses (measles, influenza, SARS-CoV-2, human rhinovirus, and adenovirus) emitted from a speaking person in a typical residential setting over a relative humidity (RH) range of 20-80% and air temperature of 20-25 °C. Besides the size transformation of virus-containing droplets due to evaporation, respiratory absorption, and then removal by gravitational settling, the modified model also considered the removal mechanism by ventilation. The trend and magnitude of RH impact depended on the respiratory virus. For rhinovirus and adenovirus humidifying the indoor air from 20/30 to 50% will be increasing the relative infection risk, however, this relative infection risk increase will be negligible for rhinovirus and weak for adenovirus. Humidification will have a potential benefit in decreasing the infection risk only for influenza when there is a large infection risk decrease for humidifying from 20 to 50%. Regardless of the dry solution composition, humidification will overall increase the infection risk via long-range airborne transmission of SARS-CoV-2. Compared to humidification at a constant ventilation rate, increasing the ventilation rate to moderate levels 0.5 → 2.0 h-1 will have a more beneficial infection risk decrease for all viruses except for influenza. Increasing the ventilation rate from low values of 0.5 h-1 to higher levels of 6 h-1 will have a dominating effect on reducing the infection risk regardless of virus type.

Experimental study on the exposure level of surgical staff to SARS-CoV-2 in operating rooms with mixing ventilation under negative pressure.

The purpose of this study was to reveal the exposure level of surgical staff to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from the patient's nose and wound during operations on COVID-19 patients. The tracer gas N2O is used to simulate SARS-CoV-2 from the patient's nose and wound. In this study, concentration levels of tracer gas were measured in the breathing zones of these surgical staff in the operating room under three pressure difference conditions: -5 pa-15 pa and -25 pa compared to the adjunction room. These influencing factors on exposure level are analyzed in terms of ventilation efficiency and the thermal plume distribution characteristics of the patient. The results show that the assistant surgeon faces 4 to 12 times higher levels of exposure to SARS-CoV-2 than other surgical staff. Increasing the pressure difference between the OR lab and adjunction room can reduce the level of exposure for the main surgeon and assistant surgeon. Turning on the cooling fan of the endoscope imager may result in a higher exposure level for the assistant surgeon. Surgical nurses outside of the surgical microenvironment are exposed to similar contaminant concentration levels in the breathing zone as in the exhaust. However, the ventilation efficiency is not constant near the surgical patient or in the rest of the room and will vary with a change in pressure difference. This may suggest that the air may not be fully mixed in the surgical microenvironment.

Infection probability under different air distribution patterns

Infectious diseases have caused significant physical harm to humans as well as enormous economic losses over the years. Effective ventilation and distribution of fresh air could help to reduce indoor cross-infection. The computational fluid dynamics (CFD) method was used in this paper to investigate airborne transmission with seven different air distribution methods. The revised Wells-Riley model, which took into account the non-uniform air distribution generated with the methods, was used to calculate the infection probability in an office room shared by ten occupants for 4 h. One of the occupants was an infector. The significance of the infector's location was studied. The obtained infection probability was compared to that obtained in the case of complete air mixing, which is uncommon in practice. Under specified conditions of this study, personalized ventilation (PV) performed the best in terms of preventing cross-infection, followed by displacement ventilation (DV), impinging jet ventilation (IJV), stratum ventilation (SV) and wall attachment ventilation (WAV). The number of infected occupants was reduced below the number obtained under the complete mixing assumption by using these air distribution methods. Mixing ventilation (MV) and diffuse ceiling ventilation (DCV) exhibited the worst performance. In comparison to the case of complete mixing the infection probability for seven out of nine susceptible occupants was higher with MV and for all occupants in the case of DCV. In SV, the position of the infector had a clear impact on the infection probability of susceptible individuals. WAV may perform better in practice if the system is well designed. The location of the exhaust outlets had a significant impact on the infection probability for DCV.

Estimating the impact of indoor relative humidity on SARS-CoV-2 airborne transmission risk using a new modification of the Wells-Riley model.

A novel modified version of the Wells-Riley model was used to estimate the impact of relative humidity (RH) on the removal of respiratory droplets containing the SARS-CoV-2 virus by deposition through gravitational settling and its inactivation by biological decay; the effect of RH on susceptibility to SARS-CoV-2 was not considered. These effects were compared with the removal achieved by increased ventilation rate with outdoor air. Modeling was performed assuming that the infected person talked continuously for 60 and 120 min. The results of modeling showed that the relative impact of RH on the infection risk depended on the ventilation rate and the size range of virus-laden droplets. A ventilation rate of 0.5 ACH, the change of RH between 20% and 53% was predicted to have a small effect on the infection risk, while at a ventilation rate of 6 ACH this change had nearly no effect. On the contrary, increasing the ventilation rate from 0.5 ACH to 6 ACH was predicted to decrease the infection risk by half which is remarkably larger effect compared with that predicted for RH. It is thus concluded that increasing the ventilation rate is more beneficial for reducing the airborne levels of SARS-CoV-2 than changing indoor RH. PRACTICAL IMPLICATIONS: The present results show that humidification to moderate levels of 40%-60% RH should not be expected to provide a significant reduction in infection risk caused by SARS-CoV-2, hence installing and running humidifiers may not be an efficient solution to reduce the risk of COVID-19 disease in indoor spaces. The results do however confirm that ventilation has a key role in controlling SARS-CoV-2 virus concentration in the air providing considerably higher benefits. The modified model developed in the present work can be used by public health experts, engineers, and epidemiologists when selecting different measures to reduce the infection risk from SARS-CoV-2 indoors allowing informed decisions concerning indoor environmental control.

Analysis of electricity use and economic impacts for buildings with electric heating under lockdown conditions: examples for educational buildings and residential buildings in Norway

The COVID-19 pandemic has caused significant impacts on energy demand in Norway and many countries. It is important to improve the existing knowledge of building operation under unforeseeable disturbances. This study aimed to identify the potential problems of electricity use patterns for four building types with electric heating: kindergartens, schools, apartments, and townhouses. By comparing the electricity profiles for the lockdown period 2020 with the normal condition in previous years, it showed that the electricity demand in the two educational institutions was almost on the same level, while there were apparent changes for the residential buildings. To estimate the energy saving potential and increase, three scenarios were developed considering different operation strategies: Scenario 1 considered operation under normal settings;Scenario 2 considered operation of educational buildings under nighttime and weekend settings;Scenario 3 considered operation of residential buildings under work-at-home conditions. Energy signature curve models were built to predict yearly demand. The results showed that the electricity demand might be reduced by one-third in educational buildings by following Scenario 2. Meanwhile, the electricity density of small apartment varied more significant than the townhouse, causing an electricity increase of 27% for the apartment and 1.3% for the townhouse under Scenario 3.