In a recent article published in the PNAS Journal, researchers investigated the dynamics of airborne diseases in a population attending consecutive meetings in enclosed spaces hosted at different time scales.
Study: Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications. Image Credit: DrazenZigic/Shutterstock.com
After the world witnessed the massive socio-economic impact of lockdowns in 2020 due to coronavirus disease 2019 (COVID-19), it is crucial to have well-defined safety guidelines for indoor gatherings.
So far, face masking, vaccination, mass-scale testing, and physical distancing during enclosed meetings have remained effective. However, there is room for improvement in mitigation measures, such as air filtration systems and encouraging hand hygiene.
The National Academies of Sciences held a workshop where they discussed multiple factors (e.g., indoor ventilation) as a determinant of the extent of exposure to a disease contagion in an enclosed space.
They emphasized the use of multiscale modeling, which embeds environmental transmission alongside human behavior to examine the spread of airborne diseases.
Clearly, there is a need for more work in this direction because currently used pairwise-interaction models of infection consider that only effective contacts, i.e., those contacts that cause new infections, transmit the disease.
There is a shortage of literature summing the effects of all the factors involved in the infection & transmission process and their comparative efficacy.
The recent advent of the virus trio, severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), influenza, and respiratory syncytial virus makes it even more urgent for studies to incorporate the interplay of these factors.
About the study
In the present study, researchers proposed a modeling framework where individuals in discrete health conditions met in different setups and interacted once or multiple times every day, for many days.
Thus, the droplets exhaled by infected individuals increased the viral load in the enclosed space, making susceptible ones inhale the virus-contaminated air.
Since the study modeling framework accounted for all environmental and group behavioral factors, it helped the researchers measure and compare the effects of all these factors on the disease transmission process at multiple scales.
The team presented theoretical and numerical findings related to trade-offs between the room and the group's behavioral characteristics to inform indoor policies that could help control disease spread in closed environments.
These factors included a meeting venue's size, ventilation system, its efficacy, air mass, meeting group size, meeting & break times, and mask and testing compliance in the group. In addition, they assessed the interactions and trade-offs for all these control variables to find the most effective policy responses for different venue settings.
Although the researchers fetched the current study data from the published literature on COVID-19, the proposed methods are widely applicable. More importantly, they focused on the epidemiological significance of policies, rather than optimizing policies, for preventing disease spread during indoor events.
The modeling framework addressed four scenarios depending on the time scales, as follows:
i) short time-scale scenario,
ii) medium short time-scale scenario,
iii) medium-long time-scale scenario, and
iv) long time-scale scenario.
It modeled the time scales in the fast dynamics of within-room transmission and the slow dynamics of disease progression among the population. These helped the researchers derive policy insights to mitigate disease spread under a certain threshold.
For instance, for the long-timescale scenario, where multiple meetings took place for many days, a 20 minutes break balanced the effect of a 40% testing reduction during long meetings. However, the same 20-minute break balanced the effect of a 60% testing reduction during shorter-duration meetings.
The study results emphasized the impact of break times, mask-wearing, and testing on the conditions that helped effectively control disease in enclosed spaces. Other critical study findings were that high air filtering efficiency became more important as meetings extended, likely because larger group sizes increased the cumulative virus stock in an enclosed space.
Similarly, the study model predicted splitting the meeting by a break was better than splitting the crowd into two meetings. It allowed better air filtering during the long break, drastically reducing the room's viral load and exerting the same effects as masking and testing.
Finally, the researchers empirically validated this modeling framework by studying the airborne disease dynamics in three case studies.
The first case study had a classroom setup where 19 students and a teacher met daily for five days a week and took one recess break every day. They found that small group sizes must also comply with face masking, and break time was critical.
Simulating the study model for the super spreading event of the Skagit Valley Choir practice held in 2020 revealed that a single break combined with medium masking compliance could have reduced the infections by 50%. The third case study involved elderly individuals living in a long-term care facility.
The model predicted that despite its small size, this group needed higher compliance to masking because they were more vulnerable to contracting infections, especially if their breathing activity was medium or high.
Overall, the study model could help validate the epidemiological repercussions of policy measures and help policy-makers account for the trade-offs between all control variables. e.g., the ventilation system's efficacy when making policies such as masking mandates, restrictions on public meetings, and instituting lockdowns.
In real-world scenarios, the members of a population could change between consecutive meetings. Also, mask-wearing behaviors in the group change over meetings depending on factors, e.g., the apprehension of the disease.
In future works, modeling frameworks should account for more complex scenarios, such as individuals' mobility across multiple meeting rooms, while studying disease transmission dynamics over consecutive meetings. These studies should also examine variations in the meeting durations and the impact of in-person meetings.
Nevertheless, the modeling framework developed in this study would remain relevant because it effectively captures the trade-offs involved in endogenizing exogenously defined conditions, such as mask compliance, masking efficacy, group size, and airmass of a meeting venue.
Dixit, A.K. et al. (2023) "Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications," Proceedings of the National Academy of Sciences, 120(16). https://www.pnas.org/doi/10.1073/pnas.2216948120