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.

Keywords: Agent-based simulation; Contact tracing; SARS-CoV-2 (COVID-19); Social distancing; Surveillance testing; Ventilation; Virus airborne transmission.

Fig. 1

A schematic diagram of the role for agent-based simulations on clinical and experimental trial design and policy assessment.

Fig. 2

A schematic view of sample quanta-cone with the height of 2.5 m and diameter of 1.2 m, derived using Monte Carlo simulation.

Fig. 3

The classroom scheme and the estimated risk map for a 49-seat classroom. Students sitting in front of infectious agents have higher risk of infection. The risk of infection is assumed to be zero for already infected agents and empty cells.

Fig. 4

Transmission risk among students – traditional and novel risk models: the orange curve shows the traditional transmission risk as a function of time. The purple curves show different possible transmission risks based on a class with randomly seated students. The closer a susceptible agent is to an infectious one, the higher the risk of transmission.

Fig. 5

Transmission risks under different number of sessions per week; (2–5) and (3–4) indicate two days a week schedule with 2,5/3,4 days in between sessions.

Fig. 6

The impact of distance and ventilation on the mean transmission risk in a 3- days a week class with no testing or contact tracing within 100 replications of the model.

Fig. 7

Epidemic size: the average number of agents experiencing each possible health status per day, for 100 replications. As more controlling policies are put into action, the epidemic size reduces.