{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1079452,0.001139842,0.003097286,0.003596096,0.00204948,0.01022706,0.004166581,0.005286695,0.002688375],"category_scores_gemma":[0.2662643,0.001424375,0.002036486,0.003648336,0.009449893,0.01494918,0.007910486,0.008895924,0.0004726054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004888174,"about_ca_system_score_gemma":0.006415528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007550277,"about_ca_topic_score_gemma":0.004462104,"domain_scores_codex":[0.9156467,0.06845327,0.004425637,0.003560771,0.007018872,0.0008947478],"domain_scores_gemma":[0.6111385,0.3554222,0.009489711,0.01148238,0.01097909,0.001488007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000650431,0.00006221301,0.002411834,0.0006337169,0.0001628833,0.0001900172,0.001474699,0.08445,0.0002793045,0.8594113,0.004165225,0.04669384],"study_design_scores_gemma":[0.000007457255,0.00001087915,0.0002626743,0.0002143495,0.00001042308,0.00003498282,0.0003754909,0.05442242,0.0001161588,0.9407121,0.003800059,0.0000330323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006890235,0.004088389,0.9397635,0.04353572,0.0002577383,0.0001175471,0.0005054274,0.00008509719,0.0047563],"genre_scores_gemma":[0.4592228,0.006597701,0.5259655,0.003744021,0.001698424,0.0006850687,0.0007854636,0.0001388703,0.001162233],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1079452,"threshold_uncertainty_score":0.5708757,"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."}}