An Agent-Based Model to Support Infection Control Strategies at School

Daniele Baccega (), Simone Pernice (), Pietro Terna (), Paolo Castagno (), Giovenale Moirano (), Lorenzo Richiardi (), Matteo Sereno (), Sergio Rabellino (), Milena Maria Maule () and Marco Beccuti ()

Journal of Artificial Societies and Social Simulation, 2022, vol. 25, issue 3, 2

Abstract: Many governments enforced physical distancing measures during the COVID-19 pandemic to avoid the collapse of often fragile and overloaded health care systems. Following the physical distancing measures, school closures seemed unavoidable to keep the transmission of the pathogen under control, given the potentially high-risk of these environments. Nevertheless, closing schools was considered an extreme and the last resort of governments, and so various non-pharmaceutical interventions in schools were implemented to reduce the risk of transmission. By means of an agent-based model, we studied the efficacy of active surveillance strategies in the school environment. Simulations settings provided hypothetical although realistic scenarios which allowed us to identify the most suitable control strategy to avoid massive school closures while adapting to contagion dynamics. Reducing risk by means of public policies explored in our study is essential for both health authorities and school administrators.

Keywords: Agent-Based Simulation; SARS-CoV-2; Non-Pharmaceutical Interventions; Surveillance Testing; School (search for similar items in EconPapers)
Date: 2022-06-30
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2022-6-2

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