{"id":"W4309923981","doi":"10.1186/s12939-022-01767-5","title":"The World Health Organization COVID-19 surveillance database","year":2022,"lang":"en","type":"article","venue":"International Journal for Equity in Health","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Affairs Canada; World Health Organization","keywords":"Public health; Pandemic; Public health surveillance; Disease surveillance; Medicine; Population; Dashboard; Environmental health; Outbreak; Demography; Global health; Health care; Disease; Geography; Coronavirus disease 2019 (COVID-19); Database; Economic growth; Infectious disease (medical specialty); Virology; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002141055,0.001426558,0.001526091,0.005079666,0.0004691996,0.001500117,0.001722983,0.001042543,0.03311337],"category_scores_gemma":[0.009026837,0.0005041367,0.0007946842,0.008088541,0.0001954191,0.001373845,0.001482089,0.001174145,0.02943048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145973,"about_ca_system_score_gemma":0.002968583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01939999,"about_ca_topic_score_gemma":0.01229477,"domain_scores_codex":[0.9977722,0.0003799786,0.0007018626,0.0005212252,0.0004384787,0.0001862534],"domain_scores_gemma":[0.9951616,0.001113062,0.0008804437,0.0007166669,0.001596283,0.0005318961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001795569,0.00004791318,0.007214771,0.001199947,0.00008122512,0.00007499677,0.00006279902,0.0005705777,0.0002947768,0.001056748,0.977623,0.01159359],"study_design_scores_gemma":[0.000309597,0.00007260558,0.03338543,0.0009175257,0.0001063511,0.000242976,0.0002250309,0.002019297,0.0006864243,0.001636563,0.960307,0.00009113087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0004401805,0.0001175352,0.000237913,0.0000817706,0.00002601577,0.000075,0.9975823,0.0002739154,0.001165304],"genre_scores_gemma":[0.0008568908,0.0001141815,0.0004433181,0.00005310513,0.00001122794,0.0001700805,0.9980497,0.00002962388,0.0002718569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03311337,"threshold_uncertainty_score":0.1107752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4422445925638603,"score_gpt":0.5837114191904956,"score_spread":0.1414668266266353,"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."}}