A recent study published in the journal Engineering delves into the complex world of assessing the transmission risk of infectious diseases in indoor spaces. With the ongoing impact of the COVID-19 pandemic, understanding how to accurately evaluate the effectiveness of non-pharmaceutical interventions (NPIs) has become crucial.
Governments worldwide implemented NPIs to control the spread of COVID-19. Many studies used simulations to measure the risk of infection transmission before and after implementing these measures. However, the choice of metric to quantify this risk can lead to different conclusions about the effectiveness of a policy.
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The research team, led by researchers from various institutions in The Republic of Korea, analyzed the correlation between different transmission-risk metrics, pedestrian environments, and types of infectious diseases. They used data generated from simulations to conduct their analysis. Five different metric types were examined, including infection-based metrics, contact-based metrics, and network-based metrics (degree centrality, betweenness centrality, and closeness centrality).
Building layout
An agent-based simulation model was developed using the Pedestrian Library feature of AnyLogic software, based on the layout of a building at Seoul National University. The model took into account lecture timetables, lecture room allocations, and agents’ behavior rules. Three environmental variables-infection transmission rate, free activity rate, and zoning-were considered in the simulations.
The results showed conflicting outcomes among the five metric types in specific environments. When the randomness of pedestrian trajectories in indoor spaces was low, closeness centrality had a higher correlation coefficient with infection-based metrics than with contact-based metrics. Also, for infectious diseases with low transmission rates, the likelihood of discrepancies between infection-based metrics and other metrics increased within the same pedestrian environment.
This study has important implications. Facility managers should not rely solely on one metric to evaluate NPIs. Instead, they need to consider the type of facility and the nature of the infectious disease when choosing a metric. For example, in environments with low pedestrian randomness like schools, contact-based metrics might be more appropriate, while in high-randomness environments like shopping malls, closeness centrality among network-based metrics could be more reliable.
Exposure time
The research also validates some assumptions in previous studies. For instance, it shows a strong correlation between infection rate and exposure time, supporting the common use of exposure time as a metric in COVID-19 studies. However, the study has limitations, such as being limited to university facilities and not fully considering the impact of indoor population density. Future research is needed to explore more diverse facilities and conditions to further enhance the understanding of disease spread in indoor spaces.
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