{"id":"W2030386659","doi":"10.3934/mbe.2013.10.1691","title":"Optimal isolation strategies of emerging infectious diseases with limited resources","year":2013,"lang":"en","type":"article","venue":"Mathematical Biosciences & Engineering","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National University of Defense Technology; Canada Research Chairs; International Development Research Centre","keywords":"Isolation (microbiology); Optimal control; Limited resources; Mathematical optimization; Population; Control (management); Patient isolation; Computer science; Outbreak; Mathematics; Biology; Statistics; Virology; Medicine; Artificial intelligence; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003206859,0.0002149501,0.0004329671,0.0001288457,0.0001110752,0.00009076944,0.0002343144,0.00005857325,0.0001361461],"category_scores_gemma":[0.002537803,0.00013504,0.00007723563,0.0005165475,0.0002328212,0.0003442848,0.0001165468,0.000114563,0.00001609594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003600798,"about_ca_system_score_gemma":0.00001901109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002708216,"about_ca_topic_score_gemma":0.000002684084,"domain_scores_codex":[0.9985309,0.00003529102,0.0004502565,0.0002754942,0.0003471395,0.000360919],"domain_scores_gemma":[0.9973831,0.002048758,0.0001555466,0.0002161063,0.00008879915,0.0001076611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003524719,0.001013198,0.03092958,0.003326221,0.0003177999,0.00001476083,0.007649994,0.1098518,0.0375629,0.8066254,0.0005398053,0.002133241],"study_design_scores_gemma":[0.0007968231,0.001093827,0.04966638,0.001049566,0.0002381192,0.00002640009,0.006889458,0.672004,0.002295112,0.2643549,0.0003832657,0.001202139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8155162,0.00007586612,0.1829008,0.0002724676,0.00003210189,0.0002710073,0.00000250278,0.0002586,0.000670483],"genre_scores_gemma":[0.9588969,0.000008144732,0.04094177,0.00002066611,0.0000345296,0.00006152363,5.664234e-7,0.00001405083,0.00002179967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5621521,"threshold_uncertainty_score":0.5506773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05105509592495362,"score_gpt":0.3175036127625334,"score_spread":0.2664485168375798,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}