{"id":"W3041150558","doi":"10.5281/zenodo.3936037","title":"A Bayesian Approach to Improving Spatial Estimates After Accounting for Misclassification Bias in Surveillance Data for COVID-19 in Philadelphia, PA","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Bayesian probability; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Econometrics; Statistics; Computer science; Geography; Medicine; Economics; Mathematics; Virology","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":[0.0664847,0.00128209,0.00246912,0.004992712,0.001615394,0.003112711,0.003651527,0.002053612,0.002200325],"category_scores_gemma":[0.1953363,0.001990272,0.00308767,0.004152289,0.001653087,0.002453352,0.004514799,0.003577898,0.0004020406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003106789,"about_ca_system_score_gemma":0.00471453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04792904,"about_ca_topic_score_gemma":0.05498988,"domain_scores_codex":[0.9467128,0.04333027,0.00242718,0.004451984,0.002528527,0.0005491487],"domain_scores_gemma":[0.8820954,0.09625724,0.006644912,0.005914229,0.008419268,0.0006689633],"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":[0.0004113428,0.0002023523,0.08618434,0.000728274,0.003279534,0.0005691939,0.00229718,0.4679696,0.001214879,0.08698583,0.007481013,0.3426764],"study_design_scores_gemma":[0.0001565677,0.0001595179,0.01649061,0.0004249453,0.000714677,0.0002940658,0.0003427021,0.8546076,0.0009278879,0.1156513,0.01009202,0.0001380321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01293317,0.0006560529,0.9838741,0.0008790646,0.00006742972,0.0001844355,0.0003491828,0.0002544454,0.0008020776],"genre_scores_gemma":[0.1968093,0.0005492614,0.7991918,0.0004778719,0.0001955178,0.0007520154,0.0009140046,0.0001250794,0.0009851515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0664847,"threshold_uncertainty_score":0.3516089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1317021171927111,"score_gpt":0.3187360134068091,"score_spread":0.1870338962140979,"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."}}