2026-06-29T20:33:17Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/118462026-06-17T00:05:32Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832

Optimizing classroom assignments to minimize epidemiological risk: the sibling rewiring problem Galán Ordax, José Manuel Díaz de la Fuente, Silvia Ahedo García, Virginia Santos Martín, José Ignacio Classroom assignment Sibling networks Epidemic risk Assignment optimization Network fragmentation Multi-objective optimization Equitable risk distribution In the context of infectious diseases, the assignment of students to classroom groups can significantly influence infection dynamics within school environments, particularly when sibling relationships introduce latent connections between otherwise unconnected groups. Traditional grouping methods and pandemic-era bubble strategies do not explicitly optimize student network structures or account for equity in exposure. This study introduces the Sibling Rewiring Problem, a novel multi-objective framework for student assignment that aims to maximize network fragmentation, reduce potential contagion pathways and minimize variance in group sizes and epidemiological exposure—thereby promoting fairness. We compared baseline, heuristic, and metaheuristic strategies in realistic school scenarios. A simple heuristic that assigns siblings to the same classroom line when feasible consistently achieves substantial network fragmentation with minimal impact on equity. Simulated Annealing further improved these results, particularly in complex configurations with densely connected sibling networks. Our findings suggest that family-aware classroom assignments can enhance epidemiological resilience while maintaining socially acceptable distributions. This approach provides a practical and scalable framework for integrating public health considerations into educational planning and may inform future decision-making in both emergency and routine contexts 2026-06-16T07:46:11Z 2026-06-16T07:46:11Z 2026-03 info:eu-repo/semantics/article https://hdl.handle.net/10259/11846 10.7717/peerj-cs.3710 2376-5992 eng PeerJ Computer Science. 2026, V. 12, art. e3710 https://doi.org/10.7717/peerj-cs.3710 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional PeerJ Inc.