{"id":"W4220666549","doi":"10.1214/21-aoas1499","title":"Bayesian adjustment for preferential testing in estimating infection fatality rates, as motivated by the COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"The Annals of Applied Statistics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Institute of Genetics; Canadian Institutes of Health Research; European Commission","keywords":"Coronavirus disease 2019 (COVID-19); Bayesian probability; Pandemic; Statistics; Case fatality rate; Econometrics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Identifiability; Demography; 2019-20 coronavirus outbreak; Population; Statistical hypothesis testing; Mathematics; Geography; Actuarial science; Medicine; Economics; Virology; Sociology; Outbreak","routes":{"ca_aff":true,"ca_fund":true,"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":[0.1348215,0.001253868,0.00246689,0.001948703,0.001373186,0.002841642,0.004420705,0.002999809,0.001960096],"category_scores_gemma":[0.3909401,0.001480212,0.002409198,0.002765444,0.005560198,0.004015801,0.003641571,0.005745742,0.0003064639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002331473,"about_ca_system_score_gemma":0.003190097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01287949,"about_ca_topic_score_gemma":0.01102256,"domain_scores_codex":[0.8821077,0.1053736,0.002272789,0.005405396,0.003685819,0.00115462],"domain_scores_gemma":[0.6324457,0.3276676,0.01509724,0.017827,0.005809401,0.001153169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001400547,0.000190391,0.08502714,0.000711577,0.00243038,0.001119086,0.001906028,0.3857392,0.002102326,0.3593467,0.006629892,0.1533968],"study_design_scores_gemma":[0.0001454095,0.0003756516,0.01688315,0.0002882406,0.0003213401,0.0006612658,0.0002143289,0.6568577,0.001371063,0.3185419,0.004200266,0.0001395315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04845202,0.001067068,0.9445403,0.002845156,0.0001676694,0.0002505838,0.000223705,0.0002533182,0.002200229],"genre_scores_gemma":[0.628529,0.0007847435,0.3653288,0.001587707,0.0002806826,0.0006427469,0.0006237563,0.0001367163,0.002085906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1348215,"threshold_uncertainty_score":0.7130127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5179263313108267,"score_gpt":0.4970631588051286,"score_spread":0.02086317250569808,"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."}}