{"id":"W4385614474","doi":"10.1016/j.idm.2023.08.002","title":"Bayesian modeling of dynamic behavioral change during an epidemic","year":2023,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Statistical Sciences Institute; University of Calgary; University of Minnesota","keywords":"Epidemic model; Computer science; Population; ALARM; Parametric model; Bayesian probability; Parametric statistics; Range (aeronautics); Statistics; Artificial intelligence; Mathematics; Engineering; Demography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000747269,0.0003014973,0.0005758488,0.0002681737,0.0003055674,0.00001929021,0.0002114862,0.0001198707,0.00002410763],"category_scores_gemma":[0.0005066979,0.0002860614,0.0002649349,0.0004685521,0.00006994818,0.0002560072,0.0001952331,0.0002467799,0.00002272208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002049704,"about_ca_system_score_gemma":0.00003510058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006785794,"about_ca_topic_score_gemma":0.0001313215,"domain_scores_codex":[0.9976891,0.0001913049,0.0006883757,0.0005576747,0.0002844018,0.0005891614],"domain_scores_gemma":[0.9983348,0.0005288806,0.0002044675,0.000520753,0.0001110528,0.000300077],"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":[0.00006103305,0.0003041466,0.02659402,0.0005045902,0.00005119464,0.00004488572,0.0009343949,0.9692186,0.0001391216,0.001317649,0.00000964784,0.000820683],"study_design_scores_gemma":[0.0002639583,0.00004569717,0.00125512,0.0001174229,0.0001172928,0.000001350915,0.00007143442,0.8456891,0.00000636464,0.1521732,0.00000260498,0.0002565364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7494578,0.0001864575,0.2489303,0.0001059371,0.000106631,0.0003840768,0.00003143583,0.0007602965,0.00003700937],"genre_scores_gemma":[0.9978569,0.0002939801,0.001328444,0.00007227278,0.0001295894,0.0002054715,0.00002226655,0.00006783829,0.00002326444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.248399,"threshold_uncertainty_score":0.9999592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3531361294602046,"score_gpt":0.4406110606615723,"score_spread":0.08747493120136773,"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."}}