COVID-19 aerosol transmission simulation-based risk analysis for in-person learning

By Dr. Ramya Dwivedi, Ph.D.Reviewed by Benedette Cuffari, M.Sc.Oct 11 2021

In a recent study published on the preprint server medRxiv*, researchers discuss their development of a simulation model to characterize the risks and uncertainties associated with infections resulting from aerosol exposure during in-person classes to guide decision-making and action under uncertainty. To this end, the current study found that the use of masks by all in colleges, combined with the installation of near-ceiling, fan-mounted ultraviolet C (UVC) systems substantially reduce risks.

Study: COVID-19 aerosol transmission simulation-based risk analysis for in-person learning. Image Credit: Prostock-studio / Shutterstock.com

This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources

Background

As educational institutions such as schools and colleges resume in-person classes after lockdowns and closures due to the coronavirus disease 2019 (COVID-19) pandemic, mitigation measures must be strategized for safe implementation and action. These may include questions regarding masking, number of students in the class, high-efficiency particulate absorbing (HEPA) filtration systems, heating, ventilation, and air condition (HVAC) system, or UVC virus inactivation in the classrooms, and testing policies.

The decisions on whether to implement these measures will inevitably impact the spread of the virus, as well as the institutional operations and cost. Therefore, it is essential to perform a risk analysis to provide qualitative and quantitative insights that would help decision-makers. However, localized conditions involve uncertainty and temporal variability about virus spread, virus prevalence in the community, and impact of infections.

COVID-19, which is caused by the severe acute respiratory disease coronavirus 2 (SARS-CoV-2), led to severe mortality and morbidity across the world. Non-pharmaceutical interventions such as lockdowns and closures of all institutions and places were implemented.

Without making precise predictions about future outcomes, the researchers noted that the study aims to provide a probabilistic understanding of potential outcomes under different interventions and rank the risks; thus, helping in prudent decisions in the circumstances.

About the study

In the current study, the researchers developed a stochastic simulation framework to estimate not just the infections, but the probability of infection, hospitalization, and death at the individual level and collective (campus) levels. They compared these risks with different potential mitigation measures.

Although the designed model is geared towards analyzing risk in a COVID-19 setting, the researchers noted that it can be used to model any other aerosol-transmitted virus, ranging from annual influenza strains to future pandemics.

Focusing only on aerosol-based transmission in the study, the researchers modeled the COVID-19 risk for one semester of operation of classes in a real college with approximately 11,000 students. Notably, the model considers immunity from vaccination and prior natural infection to provide perfect immunity for the duration of the semester. This assumption underestimates the risk of infection.

The researchers then calculated the probability of in-class infection of a non-immune individual, then the college, over course schedules, and room properties. Computing the college-cumulative number of infections, hospitalizations, and deaths, the researchers could yield information to estimate probability density functions over the individual properties and the total number of each outcome.

The researchers pointed that while they could include innumerable interventions, they limited the accounted interventions to masking mandates and the installation of UVC ceiling fans. These measures increase the rate of aerosol deactivation and also counter stagnant areas in the room.

The study showed the relative risk of in-class aerosol infection comparing scenarios with and without masking for students, thereby indicating a reduction in the probability of infected faculty. Notably, masking alone cannot counter low immunity rates in the setting employed in this study. Infection rates reduce with masking, UVC fans, and 60% immunity.

Looking at the faculty infections for high transmissibility scenarios, the researchers presented the corresponding immunity rates with and without masks and the use of UVC fans. Consistent with international policy recommendations, the researchers found that masking significantly reduces infection probability, even among non-immune students. Additionally, the researchers found that the introduction of UVC fans in every classroom reduces the infection risk more than universal masking alone.

Unlike masking, UVC fans are not directly dependent on behavioral compliance.

Using data on hospitalizations and deaths based on county health department rates, the researchers showed the associated probabilities of student hospitalizations or death under different scenarios. These scenarios included masking and no masking, use of UVC fans or not, and different immunity levels. Further, they extended the same risk analysis at the community level.

Because hospitalizations and deaths are influenced by age, the authors of the current study also factored in the age of students under different scenarios and presented the probabilities. While even at 95% immunity, the risk of hospitalizations and deaths is not zero. Thus, masking and UVC further reduce the risk by an order of magnitude.

Conclusions

The current study demonstrated that masking and UVC fans used in combination show the greatest reduction in risk under different scenarios as compared to a no-intervention scenario. The researchers recommended that the model in this study can be used in any setting such as high-risk population centers like K-12 schools, daycare facilities, or nursing homes.

Using simulation modeling, this study provided valuable data for risk analysis and therefore guides in decision making, even under uncertain circumstances.

Simulation-based risk analysis is a critical tool that helps decision-makers prepare for, mitigate against, adapt to, and recover from such events to evaluate the impacts of variables within and outside of decision-makers' control.

This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources