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