{"id":"W4211173190","doi":"10.1016/j.epidem.2022.100547","title":"Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling","year":2022,"lang":"en","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Mathematical Sciences; Research England; Medical Research Council; National Science Foundation; Royal Society; Engineering and Physical Sciences Research Council; UK Research and Innovation; Alan Turing Institute; Rural and Environment Science and Analytical Services Division; Scottish Government; Conselho Nacional de Desenvolvimento Científico e Tecnológico; ZonMw; Isaac Newton Institute for Mathematical Sciences; Wellcome Trust","keywords":"Estimation; Pandemic; Computer science; Inference; Judgement; Coronavirus disease 2019 (COVID-19); Data science; Infectious disease (medical specialty); Expert elicitation; Point estimation; Uncertainty quantification; 2019-20 coronavirus outbreak; Risk analysis (engineering); Data mining; Machine learning; Artificial intelligence; Disease; Medicine; Statistics; Engineering; Political science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004142294,0.0001210495,0.000319439,0.00008457257,0.0002490644,0.000006878055,0.0001111773,0.00006126087,0.000005229735],"category_scores_gemma":[0.01128552,0.0001157105,0.00004733324,0.0001342857,0.00003895845,0.00006950476,0.00009184298,0.0001893544,9.862565e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002266821,"about_ca_system_score_gemma":0.0000220974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000799113,"about_ca_topic_score_gemma":0.0001353538,"domain_scores_codex":[0.9984777,0.0002807298,0.0005414925,0.0003555523,0.0001302151,0.0002142941],"domain_scores_gemma":[0.9882047,0.01128446,0.00023485,0.0001848661,0.00005862129,0.000032518],"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":[0.00005338406,0.00006097661,0.009042103,0.0001974826,0.00001724565,2.617969e-7,0.001897067,0.6618085,0.00003686066,0.2982471,0.0007243201,0.02791467],"study_design_scores_gemma":[0.0001606474,0.00002863887,0.0012158,0.00001091207,0.000009644965,0.000001048028,0.000406838,0.5402572,0.000001752874,0.4556224,0.002207196,0.00007788261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.229734,0.003622119,0.7559496,0.009319359,0.0001276858,0.0009311179,0.00002847861,0.000154511,0.0001331023],"genre_scores_gemma":[0.9433483,0.001922175,0.05360791,0.000420161,0.00003960955,0.000586815,0.00003127739,0.00001785511,0.0000259068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7136143,"threshold_uncertainty_score":0.9970428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6439689404182938,"score_gpt":0.4774104970680796,"score_spread":0.1665584433502141,"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."}}