{"id":"W3091278540","doi":"10.1017/s095026882000237x","title":"Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced <i>α</i>-Sutte Indicator","year":2020,"lang":"en","type":"article","venue":"Epidemiology and Infection","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Statistics; Mean squared error; Mean absolute percentage error; Coronavirus disease 2019 (COVID-19); Epidemiology; Mortality rate; Demography; Medicine; Time series; Mathematics; Disease; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00959442,0.0002524257,0.0009112498,0.00003862382,0.0006614233,0.000004841919,0.0001672938,0.0002491802,0.00006393233],"category_scores_gemma":[0.1360753,0.0001236331,0.0001568734,0.0002802501,0.001358312,0.0000989488,0.0003370724,0.0005290531,8.859803e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005228725,"about_ca_system_score_gemma":0.00003343378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002689993,"about_ca_topic_score_gemma":0.00004831051,"domain_scores_codex":[0.9944462,0.003550156,0.0009869895,0.0005432095,0.00008716607,0.000386347],"domain_scores_gemma":[0.9611365,0.03747329,0.0008816122,0.0002713675,0.00003779979,0.000199431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006409255,0.00002212508,0.9795648,0.0003132078,0.0000896772,0.000001245055,0.0004168725,0.0006449407,0.00007511131,0.01447691,0.001369278,0.002961676],"study_design_scores_gemma":[0.0004445468,0.0004226638,0.7777302,0.00004190775,0.0002820031,0.0000486943,0.0001221701,0.03364803,0.00002905338,0.1812612,0.00572748,0.0002421297],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284063,0.002032372,0.04125527,0.02759248,0.0001037151,0.0003570927,0.00001570355,0.00009065466,0.0001464122],"genre_scores_gemma":[0.9813482,0.001697671,0.002425194,0.01428373,0.0001716554,0.00005048778,0.000002473168,0.000009428333,0.00001115373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2018347,"threshold_uncertainty_score":0.8712019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4383810400730104,"score_gpt":0.4703488192637402,"score_spread":0.03196777919072979,"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."}}