{"id":"W3175659461","doi":"10.1016/j.cegh.2021.100811","title":"Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19","year":2021,"lang":"en","type":"article","venue":"Clinical Epidemiology and Global Health","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Socioeconomic status; Environmental health; Coronavirus disease 2019 (COVID-19); Pandemic; Medicine; Overweight; Demography; Epidemiology; Geography; Body mass index; Disease; Population; Internal medicine","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":[0.002598906,0.0004319295,0.0003386369,0.0004580192,0.0005263007,0.0007747104,0.0004664261,0.0006038055,0.003051916],"category_scores_gemma":[0.009442416,0.0001652456,0.001254988,0.0006405617,0.0007007445,0.0004997634,0.001316235,0.0008568252,0.0001267396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118976,"about_ca_system_score_gemma":0.001791606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01710406,"about_ca_topic_score_gemma":0.01875706,"domain_scores_codex":[0.9956827,0.002716957,0.0001372553,0.0003106457,0.0002184278,0.0009340544],"domain_scores_gemma":[0.9948457,0.001908144,0.001795138,0.0002868131,0.0003460601,0.0008180088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001266014,0.0010802,0.976984,0.00009004638,0.0006315158,0.0001492823,0.0002328408,0.00103822,0.0002320791,0.0003066265,0.000400814,0.01758856],"study_design_scores_gemma":[0.00002540299,0.0006020325,0.9974257,0.00002816216,0.0001483888,0.0000279174,0.0003536294,0.0008400275,0.0001261797,0.0001144245,0.000302546,0.000005547897],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997891,0.0004810916,0.0001520952,0.0004735706,0.00002136408,0.00002278011,0.0001859036,0.000004695798,0.0007675145],"genre_scores_gemma":[0.999432,0.000143889,0.00008182446,0.00006903604,0.0000124847,0.00001499702,0.0001036737,0.000001828745,0.0001401727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01710406,"threshold_uncertainty_score":0.03400898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.334053459768374,"score_gpt":0.5589831954445408,"score_spread":0.2249297356761668,"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."}}