{"id":"W2086022505","doi":"10.5210/ojphi.v5i1.4574","title":"Bayesian Contact Tracing for Communicable Respiratory Disease","year":2013,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contact tracing; Computer science; Communicable disease; Bayesian probability; Population; Probabilistic logic; Dynamic Bayesian network; Pandemic; Tracing; Public health; Data science; Artificial intelligence; Data mining; Machine learning; Coronavirus disease 2019 (COVID-19); Medicine; Disease; Infectious disease (medical specialty); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001938222,0.000444528,0.0006941936,0.001734603,0.0006187591,0.0009443932,0.001016128,0.0009937927,0.001504904],"category_scores_gemma":[0.0175583,0.0004986385,0.0006051418,0.00105258,0.0004653173,0.001389497,0.001131439,0.0009275404,0.0003623916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120451,"about_ca_system_score_gemma":0.001194571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01482592,"about_ca_topic_score_gemma":0.01491242,"domain_scores_codex":[0.9986182,0.0005353119,0.00007856316,0.0002821096,0.0004162429,0.00006948359],"domain_scores_gemma":[0.9943224,0.004409554,0.0004522481,0.0003078547,0.0003989944,0.0001090311],"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":[0.0002318706,0.0002029471,0.03220545,0.0002462548,0.0001849383,0.0002917337,0.0004038941,0.6514416,0.003620401,0.04581462,0.004864559,0.2604918],"study_design_scores_gemma":[0.000007197427,0.00001116878,0.001155857,0.0000109184,0.000009826483,0.00004812861,0.00001280127,0.981278,0.0005279954,0.0157883,0.001142096,0.000007673379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0224493,0.0004533827,0.9740795,0.000466164,0.00002213677,0.00005831595,0.0003722816,0.0008379773,0.001260944],"genre_scores_gemma":[0.7498583,0.000668065,0.245418,0.0001928393,0.00006798933,0.0001719631,0.001203783,0.0001037023,0.002315378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01482592,"threshold_uncertainty_score":0.02947921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09570321424380361,"score_gpt":0.3745769563889753,"score_spread":0.2788737421451717,"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."}}