Highlights

In brief

The researchers' model could improve public health measures by providing simpler, more intuitive assessments of COVID-19 transmission risk in community spaces.

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Going with the flow

13 Oct 2022

A*STAR researchers blend the physics of particle dynamics with complex computational simulations to gauge the risk of catching COVID-19 in different social settings.

Just as the fluid nature of air carries planes to their destinations, it also sends virus-laden droplets from coughs and sneezes drifting toward others with startling efficiency. This is why when a new airborne virus emerges, public health authorities must find ways to optimally curb the spread of infections from person to person.

Chin Chun Ooi, a research scientist at A*STAR’s Institute of High Performance Computing (IHPC), said science-backed public health measures are vital for minimising case numbers, particularly during the early days of the pandemic. However, social distancing and wearing masks are not one-size-fits-all solutions. Instead, their effectiveness is impacted by a plethora of variables in different real-world scenarios.

In collaboration with Singapore’s Ministry of Health, National Centre for Infectious Diseases and Land Transport Authority, Ooi led a study on new methods for assessing infection risks in various indoor settings.

“Critical questions such as the minimum viral dose required for infection and viral load emitted when individuals cough, talk, or exercise and their impact on infection risk needed to be answered,” said Ooi. To that end, Ooi and his colleagues designed a two-pronged approach to build their risk assessment framework, combining measurements of how droplets move in air with advanced computational simulations.

The researchers used an everyday situation as their test case: a ride home on a double-decker public bus. First, they collected data on how particles circulated onboard with the help of a fog machine and a small fleet of particle detectors. They then modified the setup to simulate unique environmental conditions, such as with the windows closed and the air conditioning on and placing the COVID-19-infected individual in different locations.

The study’s results support the importance of mask-wearing: the chances of transmission were greatly reduced for those who masked up. Interestingly, seat choice also influenced the chances of catching COVID-19. Passengers seated behind an infected individual were at higher risk due to the movement of air in the enclosed environment.

According to Ooi, these results highlight risk assessment models as vital tools for protecting vulnerable communities against viral threats. “We propose a risk stratification framework that is simple, intuitive, and easily interpretable, facilitating communication and decision-making,” explained Ooi.

The virus has evolved since the study was conducted, sprouting multiple variants of concern with higher transmission rates and prompting the need for updates to the existing risk framework.

“The viral load thresholds are specific to early variants and will need to be updated with data collected about how more recent variants spread,” said Ooi, adding that these efforts could help to refine and enhance the accuracy of their risk assessment methodology under current circumstances.

The A*STAR-affiliated researchers contributing to this research are from the Institute of High Performance Computing (IHPC) and the Institute of Materials Research and Engineering (IMRE).

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References

Ooi, C.C., Suwardi, A., Ou Yang, Z. L., Xu. G., Tan, C.K.I., et al. Risk assessment of airborne COVID-19 exposure in social settings, Physics of Fluids 33, 087118 (2021) | article

About the Researcher

Chin Chun Ooi obtained his PhD degree in chemical engineering from Stanford University in 2016. He is currently a Research Scientist at the Institute of High Performance Computing (IHPC) working on computational fluid dynamics (CFD). His current research includes CFD simulations of urban scenarios for natural ventilation and wind-driven rain evaluation and optimization.

This article was made for A*STAR Research by Wildtype Media Group