{"id":"W4220890518","doi":"10.1016/j.idm.2022.02.001","title":"Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation","year":2022,"lang":"en","type":"article","venue":"Infectious Disease Modelling","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Epidemic model; Bayesian probability; Incubation period; Estimation; Geography; Transmission (telecommunications); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Infectious disease (medical specialty); Econometrics; Computer science; Statistics; Operations research; Outbreak; Mathematics; Incubation; Demography; Virology; Biology; Engineering; Medicine; Telecommunications; Disease; Sociology","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001657735,0.0003638392,0.0005239599,0.0002198062,0.001338606,0.00007547337,0.0001970208,0.00007483837,0.00006044385],"category_scores_gemma":[0.0009635088,0.0003679185,0.000151785,0.0002972787,0.00008819549,0.000235984,0.0002985152,0.0004557614,0.000004779823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004160873,"about_ca_system_score_gemma":0.00007978494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003460285,"about_ca_topic_score_gemma":0.0000209607,"domain_scores_codex":[0.9969936,0.0006800818,0.0006662469,0.0007638891,0.0003950861,0.0005010681],"domain_scores_gemma":[0.9967062,0.002032899,0.0003559234,0.0004503419,0.00006531653,0.0003893441],"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":[0.0001482076,0.0001599125,0.003990627,0.0001073364,0.00004954966,0.00004759159,0.00133684,0.9807799,0.00003331736,0.01289284,0.00002505445,0.0004288047],"study_design_scores_gemma":[0.0002548681,0.00009079113,0.00001933137,0.00002809795,0.00009721104,0.0000164685,0.00007435475,0.6678274,0.00001413554,0.3312663,0.00003285243,0.0002782392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4637829,0.0002973556,0.5349429,0.0001346187,0.0001240692,0.0003379672,0.00001818805,0.0003167358,0.00004532434],"genre_scores_gemma":[0.9755264,0.00005804077,0.02356579,0.0004140596,0.00008998789,0.00021486,0.00002039077,0.00006728102,0.00004321833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5117435,"threshold_uncertainty_score":0.9999615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1568756548716603,"score_gpt":0.3685130151835286,"score_spread":0.2116373603118684,"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."}}