{"id":"W3161025254","doi":"10.1101/2021.05.12.21256975","title":"Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Institute of Genetics; Canadian Institutes of Health Research; European Commission","keywords":"Per capita; Credible interval; Population; Gross domestic product; Demography; Seroprevalence; Bayesian probability; Case fatality rate; Statistics; Econometrics; Medicine; Actuarial science; Mathematics; Economics; Economic growth","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04527953,0.0004483457,0.001148552,0.00008308305,0.0007585487,0.0001943137,0.004323619,0.0002122414,0.00002913438],"category_scores_gemma":[0.1840098,0.0002785868,0.00009638161,0.0004979231,0.0003328185,0.0004470631,0.0180774,0.001834139,5.9848e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724189,"about_ca_system_score_gemma":0.0001714222,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05527094,"about_ca_topic_score_gemma":0.07463703,"domain_scores_codex":[0.9797002,0.01651748,0.001589882,0.001183186,0.000660432,0.0003488085],"domain_scores_gemma":[0.9312083,0.05726693,0.001089332,0.01019537,0.0001362803,0.0001037929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002099924,0.0003424451,0.9904925,0.002641885,0.0001812173,0.00001360193,0.004066858,0.001537965,0.000008117559,0.00006310512,0.00005292974,0.0005784187],"study_design_scores_gemma":[0.0001297709,0.00003027349,0.9545166,0.0005637305,0.000580914,0.000003918388,0.002697985,0.02191273,0.000007947682,0.0192473,0.00003366071,0.0002751513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784728,0.000600346,0.01697159,0.00147315,0.0001128263,0.001884025,0.0004015396,0.00007434727,0.000009372403],"genre_scores_gemma":[0.997153,0.00145816,0.000514312,0.0004097118,0.0000691159,0.0001959827,0.0001762792,0.00002236409,0.000001058916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1387303,"threshold_uncertainty_score":0.9999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6653979398143507,"score_gpt":0.5288536251553186,"score_spread":0.1365443146590321,"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."}}