{"doi":"10.1098/rsos.231842","title":"Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis","abstract":"The COVID-19 pandemic presented significant challenges in educational settings. Schools implemented a variety of COVID-19 mitigation strategies, some of which were controversial due to potential disruptions to in-person learning. We developed an agent-based model of COVID-19 in a US high school setting to evaluate potential trade-offs between preventing COVID-19 infections versus avoiding in-person learning loss under different mitigation policies in a post-Omicron context. Mitigation policies included isolation alone and in combination with quarantine of exposed students, weekly testing of all students or testing of exposed students ('test-to-stay') under different scenarios of mask use and booster dose uptake. Outcomes were simulated over an 11 week trimester. We found that requiring a full 5 or 10 day quarantine of exposed students reduced COVID-19 infections by five to sevenfold relative to isolation alone, but at a cost of nearly 40% learning days lost. Test-to-stay achieved nearly the same level of infection reduction with lower levels of learning loss. Weekly testing also reduced COVID-19 infections but was less effective and incurred higher learning loss than test-to-stay. Universal masking and increased vaccination not only reduced infections at no cost to learning but also synergized with other strategies to reduce trade-offs.","journal":"Royal Society Open Science","year":2025,"id":564611,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.944,"is_data_producer":true,"deposit_databanks":{"figshare":["10.6084/m9.figshare.c.7774676"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":371767,"name":"Eva A. Enns","orcid":"0000-0003-0693-7358","position":1,"is_corresponding":false},{"id":420682,"name":"Kenneth Chen","orcid":"0000-0001-7391-977X","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T02:56:24.872312Z","pmid":"40271137","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}