{"id":"W3199679914","doi":"10.1109/tetci.2021.3107496","title":"Optimization of Infectious Disease Prevention and Control Policies Using Artificial Life","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; McGill University","funders":"","keywords":"Computer science; Intervention (counseling); Population; Infectious disease (medical specialty); Control (management); Management science; Risk analysis (engineering); Pandemic; Disease; Artificial intelligence; Machine learning; Coronavirus disease 2019 (COVID-19); Medicine; Economics; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002378155,0.0001098348,0.0002204715,0.0001234131,0.0001297967,0.00001549057,0.00005211583,0.00004937661,0.00005151406],"category_scores_gemma":[0.000632727,0.0001147291,0.00007362373,0.0002968654,0.00009498327,0.00007489073,0.00000367826,0.0001360544,7.430737e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007710415,"about_ca_system_score_gemma":0.000105584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005491486,"about_ca_topic_score_gemma":0.00006218961,"domain_scores_codex":[0.9988399,0.0001579419,0.000484314,0.0002173243,0.0001764872,0.0001240665],"domain_scores_gemma":[0.9981894,0.001349735,0.0001244156,0.00009932559,0.0001811663,0.00005591731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002698157,0.0002121781,0.0007446012,0.00006609338,0.0000331425,0.000002411943,0.000189757,0.9695867,0.00002362376,0.02588189,0.000002044755,0.003230601],"study_design_scores_gemma":[0.0001008973,0.0000340657,0.0008593419,0.00008144555,0.00003953558,0.000001547739,0.0001002785,0.7007239,0.0004697877,0.2974879,0.000005650266,0.00009562157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08841048,0.00008009883,0.9100883,0.001030849,0.0001860307,0.0001394833,0.00001229164,0.00003129861,0.00002115834],"genre_scores_gemma":[0.9691461,0.00007452306,0.03049553,0.0002022348,0.00004116812,0.00001416231,0.000001862565,0.000007721304,0.00001670988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8807356,"threshold_uncertainty_score":0.4678516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2018713353688281,"score_gpt":0.4359538708034095,"score_spread":0.2340825354345814,"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."}}