Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk

Abstract

Many schools and universities have seen a significant increase in the spread of COVID-19. As such, a number of non-pharmaceutical interventions have been proposed including distancing requirements, surveillance testing, and updating ventilation systems. Unfortunately, there is limited guidance for which policy or set of policies are most effective for a specific school system. We develop a novel approach to model the spread of SARS-CoV-2 quanta in a closed classroom environment that extends traditional transmission models that assume uniform mixing through air recirculation by including the local spread of quanta from a contagious source. In addition, the behavior of students with respect to guideline compliance was modeled through an agent-based simulation. Estimated infection rates were on average lower using traditional transmission models compared to our approach. Further, we found that although ventilation changes were effective at reducing mean transmission risk, it had much less impact than distancing practices. Duration of the class was an important factor in determining the transmission risk. For the same total number of semester hours for a class, delivering lectures more frequently for shorter durations was preferable to less frequently with longer durations. Finally, as expected, as the contact tracing level increased, more infectious students were identified and removed from the environment and the spread slowed, though there were diminishing returns. These findings can help provide guidance as to which school-based policies would be most effective at reducing risk and can be used in a cost/comparative effectiveness estimation study given local costs and constraints.

ArchABM: an agent-based simulator of human interaction with the built environment. $CO_2$ and viral load analysis for indoor air quality

“…In comparison to departments D1-D4, the reduction in average quanta inhaled by departments D5-D7 is nearly doubled. Interestingly, other ABM-based studies found similar results when analysing the duration of classes in schools [36], where shorter classes were preferable. The remaining experiments (larger building, separate workspaces, better natural ventilation, better mechanical ventilation, and wearing masks), do not show any differential aspects among departments, however, wearing masks has the largest impact on reducing quanta levels across all departments.…”

“…Establishing a mandatory mask policy has no effect on the CO 2 level but almost completely eliminates the quanta concentration from the building, as predicted by the aerosol model developed by Peng and Jimenez [16]. Other ABM models from the literature, such as [33,34,35,36], have made similar observations regarding the use of masks.…”

“…Some other studies apply (pedestrian) movement models to simulate indoor movement patterns without taking into account virus-related parameters [31,32]. When simulating indoor environments, only a few recent studies explicitly address airborne transmission via aerosols in their ABM [33,34,35,36]. While these studies use sophisticated aerosol models, most of them focus on modeling one single room as the simulation environment (simplified supermarket, choir practice, restaurant, classroom) without taking into account the complex human-building-interaction patterns that may emerge when agents move around a building to different rooms for carrying out diverse activities while being in contact with other agents.…”

Masking significantly reduces, but does not eliminate COVID-19 infection in a spatial agent-based simulation of a University dormitory floor

“…With limited data due to the still-nascent rise of this pandemic, simulation models can be particularly helpful in making evidence-based decisions. Although many models for COVID have been developed or adapted in the past year and a half (e.g., [2][3][4][5][6][7]), there is a lack of models that are both agent-based (modeling individual people) and discrete-space (modeling actual, rather than idealized, spaces), a combination which helps to apply simulation to real-world contexts. One model that fills this gap is COVID-ADAPT.…”

Agent-Based Simulation of Virus Testing in Certain-Exposure Time through Community Health Service Centers’ Evaluation—A Case Study of Wuhan

“…With the agent model, some scholars have carried out the COVID-19 transmission simulation at a macro level [20]. However, more scholars use the agent model to study the spread and prevention and control of the COVID-19 in hospitals, schools, supermarkets, and other large public spaces by virtue of its advantages in micro-scale simulation [21][22][23][24][25][26]. In the most exciting research [27], the blood collection and diagnostic flow system in hospitals were optimized by using this system.…”