{"id":"W4288050389","doi":"10.1002/cjs.11723","title":"Extended Bayesian endemic–epidemic models to incorporate mobility data into COVID‐19 forecasting","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Canada First Research Excellence Fund","keywords":"Coronavirus disease 2019 (COVID-19); Bayesian probability; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Econometrics; Pandemic; Computer science; Artificial intelligence; Virology; Mathematics; Medicine; Outbreak; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00903588,0.0008863579,0.0015959,0.00206883,0.000719711,0.001573137,0.003322594,0.001835758,0.003888254],"category_scores_gemma":[0.02629504,0.001088813,0.001397917,0.002573275,0.0007519804,0.002451786,0.001551896,0.002666059,0.0005865918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002076671,"about_ca_system_score_gemma":0.002352891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09825895,"about_ca_topic_score_gemma":0.09897824,"domain_scores_codex":[0.9978839,0.001164203,0.000138085,0.0003814263,0.0002510347,0.0001814347],"domain_scores_gemma":[0.991532,0.006287009,0.0007503131,0.00035166,0.0009078344,0.0001710745],"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":[0.0000412847,0.0000459254,0.005347042,0.0000412424,0.00009564622,0.00008389869,0.0001422836,0.9257171,0.000146855,0.04373161,0.001749713,0.0228573],"study_design_scores_gemma":[0.000009244372,0.000008184539,0.0005185177,0.00001173072,0.00001364038,0.00001222923,0.00001534267,0.9820892,0.00003137044,0.01648101,0.0007975856,0.00001193019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06135152,0.0006801396,0.9317813,0.001325056,0.0001410412,0.0001459918,0.001272479,0.000335124,0.002967302],"genre_scores_gemma":[0.7509796,0.001247885,0.2323696,0.0004460288,0.0002787652,0.0004571957,0.002953277,0.0001594125,0.01110819],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09825895,"threshold_uncertainty_score":0.195374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5063748120552753,"score_gpt":0.4172264745248464,"score_spread":0.08914833753042894,"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."}}