Publication
School's Out? Simulating Schooling Strategies During COVID-19
Lukas Tapp; Veronika Kurchyna; Falco Nogatz; Jan Ole Berndt; Ingo Timm
In: Multi-Agent-Based Simulation XXIII. International Workshop on Multi-Agent Systems and Agent-Based Simulation (MABS-2022), located at AAMAS 2022, May 8-9, Auckland, New Zealand, Pages 95-106, Lecture Notes in Artificial Intelligence (LNAI), Springer, 2023.
Abstract
Multi-agent based systems offer the possibility to examine the effects of policies down to specific target groups while also considering the effects on a population-level scale. To examine the impact of different schooling strategies, an agent-based model is used in the context of the COVID-19 pandemic using a German city as an example. The simulation experiments show that reducing the class size by rotating weekly between in-person classes and online schooling is effective at preventing infections while driving up the detection rate among children through testing during weeks of in-person attendance. While open schools lead to higher infection rates, a surprising result of this study is that school rotation is almost as effective at lowering infections among both the student population and the general population as closing schools. Due to the continued testing of attending students, the overall infections in the general population are even lower in a school rotation scenario, showcasing the potential for emergent behaviors in agent-based models.