Airborne transmission of COVID-19 virus in enclosed spaces: An overview of research methods

Since the outbreak of COVID-19 in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has spread worldwide. This study summarized the transmission mechanisms of COVID-19 and their main influencing factors, such as airflow patterns, air temperature, relative humidity, and social distancing. The transmission characteristics in existing cases are providing more and more evidence that SARS CoV-2 can be transmitted through the air. This investigation reviewed probabilistic and deterministic research methods, such as the Wells-Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method, and the experimental approach, that have been used for studying the airborne transmission mechanism. The Wells-Riley equation and dose-response model are typically used for the assessment of the average infection risk. Only in combination with the Eulerian method or the Lagrangian method can these two methods obtain the spatial distribution of airborne particles' concentration and infection risk. In contrast with the Eulerian and Lagrangian methods, the Monte-Carlo model is suitable for studying the infection risk when the behavior of individuals is highly random. Although researchers tend to use numerical methods to study the airborne transmission mechanism of COVID-19, an experimental approach could often provide stronger evidence to prove the possibility of airborne transmission than a simple numerical model. All in all, the reviewed methods are helpful in the study of the airborne transmission mechanism of COVID-19 and epidemic prevention and control.

A coupled Computational Fluid Dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains

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.

Estimating aerosol transmission risk of SARS-CoV-2 in New York City public schools during reopening

The objective of this study was to estimate the risk of SARS-CoV-2 transmission among students and teachers in New York City public schools, the largest school system in the US. Classroom measurements conducted from December 2017 to September 2018 were used to estimate risk of SARS-CoV-2 transmission using a modified Wells-Riley equation under a steady-state conditions and varying exposure scenarios (infectious student versus teacher, susceptible student versus teacher, with and without masks). We then used multivariable linear regression with GEE to identify school and classroom factors that impact transmission risk. Overall, 101 classrooms in 19 schools were assessed, 86 during the heating season, 69 during cooling season, and 54 during both. The mean probability of transmission was generally low but varied by scenario (range: 0.0015-0.81). Transmission rates were higher during the heating season (beta=0.108, p=0.010), in schools in higher income neighborhoods (>80K versus 20K-40K beta=0.196, p<0.001) and newer buildings (<50 years beta=0.237, p=<0.001; 50-99 years beta=0.230, p=0.013 versus 100+ years) and lower in schools with mechanical ventilation (beta=0.141, p=0.057). Surprisingly, schools located in older buildings and lower-income neighborhoods had lower transmission probabilities, likely due to the greater outdoor airflow associated with an older, non-renovated buildings that allow air to leak in (i.e. drafty buildings). Despite the generally low risk of school-based transmission found in this study, with SARS-CoV-2 prevalence rising in New York City this risk will increase and additional mitigation steps should be implemented in schools now.

Association of the infection probability of COVID-19 with ventilation rates in confined spaces

A growing number of cases have proved the possibility of airborne transmission of the coronavirus disease 2019 (COVID-19). Ensuring an adequate ventilation rate is essential to reduce the risk of infection in confined spaces. In this study, we estimated the association between the infection probability and ventilation rates with the Wells-Riley equation, where the quantum generation rate (q) by a COVID-19 infector was obtained using a reproductive number-based fitting approach. The estimated q value of COVID-19 is 14-48 h^-1. To ensure an infection probability of less than 1%, a ventilation rate larger than common values (100-350 m³/h per infector and 1200-4000 m³/h per infector for 0.25 h and 3 h of exposure, respectively) is required. If the infector and susceptible person wear masks, then the ventilation rate ensuring a less than 1% infection probability can be reduced to a quarter respectively, which is easier to achieve by the normal ventilation mode applied in typical scenarios, including offices, classrooms, buses, and aircraft cabins. Strict preventive measures (e.g., wearing masks and preventing asymptomatic infectors from entering public spaces using tests) that have been widely adopted should be effective in reducing the risk of infection in confined spaces.

A human behavior integrated hierarchical model of airborne disease transmission in a large city

Epidemics of infectious diseases such as SARS, H1N1, and MERS threaten public health, particularly in large cities such as Hong Kong. We constructed a human behavior integrated hierarchical (HiHi) model based on the SIR (Susceptible, Infectious, and Recovered) model, the Wells-Riley equation, and population movement considering both spatial and temporal dimensions. The model considers more than 7 million people, 3 million indoor environments, and 2566 public transport routes in Hong Kong. Smallpox, which could be spread through airborne routes, is studied as an example. The simulation is based on people's daily commutes and indoor human behaviors, which were summarized by mathematical patterns. We found that 59.6%, 18.1%, and 13.4% of patients become infected in their homes, offices, and schools, respectively. If both work stoppage and school closure measures are taken when the number of infected people is greater than 1000, an infectious disease will be effectively controlled after 2 months. The peak number of infected people will be reduced by 25% compared to taking no action, and the time of peak infections will be delayed by about 40 days if 90% of the infected people go to hospital during the infectious period. When ventilation rates in indoor environments increase to five times their default settings, smallpox will be naturally controlled. Residents of Kowloon and the north part of Hong Kong Island have a high risk of infection from airborne infectious diseases. Our HiHi model reduces the calculation time for infection rates to an acceptable level while preserving accuracy.

Tuberculosis transmission in public locations in Tanzania: A novel approach to studying airborne disease transmission

OBJECTIVES

For tuberculosis (TB) transmission to occur, an uninfected individual must inhale the previously infected breath. Our objective was to identify potential TB transmission hotspots in metropolitan city of Dar es Salaam, Tanzania and to model the annual risk of TB transmission in different locations of public importance.

METHODS

We collected indoor carbon dioxide (CO₂) data from markets, prisons, night clubs, public transportation, religious and social halls, and from schools. Study volunteers recorded social contacts at each of the locations. We then estimated the annual risks of TB transmission using a modified Wells-Riley equation for different locations.

RESULTS

The annual risks of TB transmission were highest among prison inmates (41.6%) and drivers (20.3%) in public transport. Lower transmission risks were found in central markets (4.8% for traders, but 0.5% for their customers), passengers on public transport (2.4%), public schools (4.0%), nightclubs (1.7%), religious (0.13%), and social halls (0.12%).

CONCLUSION

For the first time in a country representative of sub-Saharan Africa, we modelled the risk of TB transmission in important public locations by using a novel approach of studying airborne transmission. This approach can guide identification of TB transmission hotspots and targeted interventions to reach WHO's ambitious End TB targets.