Concordia Researchers Unveil Innovative Method to Track Disease Spread

Researchers at Concordia University have developed a groundbreaking method for tracking how disease-causing particles, or pathogens, spread within indoor environments. This innovative tool could be instrumental in managing future outbreaks of contagious diseases, including COVID-19. The findings were published in the journal Building and Environment.

Utilizing real-time camera and sensor technology, the method monitors infected individuals and employs algorithm-driven models to evaluate air and pathogen dispersal. This dual approach not only assesses infection risk levels but also enhances ventilation systems to optimize airflow and minimize the risk of transmission.

Zeinab Deldoost, the study’s lead author and PhD candidate, highlighted the efficiency of their method. “Our novel method greatly reduces the simulation time found in other studies, giving us a better estimation of whether a location that has been exposed to pathogens still poses a risk,” she explained. Traditional models often capture airborne spread for less than a minute, while this new system can simulate pathogen dispersal over much longer periods.

Enhanced Tracking Capabilities

The research team, which includes co-author Fariborz Haghighat, a professor in the Department of Building, Civil and Environmental Engineering, emphasized the importance of monitoring individuals who are infected. “If we know a person is sick, then this system helps us monitor them and find out the dimensional dispersion of the pathogen around them,” Haghighat stated.

The system is designed to function optimally in dynamic environments, such as hospitals, where occupancy levels fluctuate. The model simplifies airflow calculations by treating a person as a massless moving emission source, which allows for consistent airflow measurements and studies pathogen dispersal from this moving source.

The researchers validated their approach by demonstrating that a person’s presence only temporarily disrupts airflow. After an individual vacates a room, the airflow stabilizes within approximately 40 seconds, with the disturbance extending only about one meter from their path. Consequently, the impact on pathogen dispersal over larger areas and extended durations is minimal.

The model’s efficiency is evident; it can simulate one second of pathogen dispersion in just 3.8 seconds on a standard laptop. This rapid processing power enables decision-makers to make near real-time assessments about infection risks in indoor spaces.

Future Implications for Public Health

Co-author Fuzhan Nasiri, also a professor in the same department, expressed optimism about the system’s potential for broader applications. “If we could use this simulation over an extended period under different scenarios, we could generate enough data to represent various movement and dispersion patterns,” he noted.

Such data could be invaluable for training artificial intelligence systems, allowing future users to bypass extensive simulations while still achieving accurate results.

This research received support from a Natural Sciences and Engineering Research Council of Canada Discovery Research Grant, underscoring its significance in advancing public health technology. As the world continues to navigate the complexities of disease transmission, tools like this offer a promising avenue for enhancing indoor safety.

For further details, refer to the article titled “Real-time analysis of pathogen dispersion patterns resulting from a moving infectious person.”