{"id":"W4396607316","doi":"10.1016/j.idm.2024.04.002","title":"A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters","year":2024,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa; Royal Military College of Canada; Carleton University","funders":"","keywords":"LTI system theory; Nonlinear system; Invariant (physics); Epidemic model; Applied mathematics; Bayesian probability; Computer science; Estimation theory; Mathematics; Statistical physics; Algorithm; Artificial intelligence; Linear system; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008094856,0.001658577,0.002054442,0.001821161,0.001048095,0.003300761,0.005021079,0.003533857,0.005040038],"category_scores_gemma":[0.02187563,0.001745603,0.002698596,0.001957593,0.002338085,0.004387004,0.003509203,0.004783274,0.001206325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003232402,"about_ca_system_score_gemma":0.003512603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01370787,"about_ca_topic_score_gemma":0.00967812,"domain_scores_codex":[0.9974477,0.001287087,0.0001187577,0.0004814878,0.0005094602,0.0001554642],"domain_scores_gemma":[0.9915965,0.005918986,0.00102518,0.0004180591,0.0008261306,0.0002150303],"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":[0.00001768487,0.00002595465,0.0004688138,0.00008647663,0.0000647409,0.0000955767,0.0001255504,0.6797198,0.0003700154,0.3055921,0.001037273,0.01239606],"study_design_scores_gemma":[0.000006771212,0.0000140629,0.0001045227,0.0000354155,0.00002051424,0.00003728904,0.00001584595,0.8972861,0.000130714,0.0995441,0.002773265,0.00003145612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009505827,0.0002185401,0.9971281,0.0003003852,0.00002300055,0.00002234713,0.00007326274,0.0000790281,0.001204837],"genre_scores_gemma":[0.274428,0.003426563,0.7051904,0.0008470022,0.0005978475,0.001109003,0.001008068,0.0004541141,0.01293904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01370787,"threshold_uncertainty_score":0.04281026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1254371695168849,"score_gpt":0.3502335049037069,"score_spread":0.224796335386822,"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."}}