{"id":"W3173743723","doi":"10.1136/bmj.n1544","title":"Beyond the numbers: understanding the diversity of covid-19 epidemiology and response in South Asia","year":2021,"lang":"en","type":"article","venue":"BMJ","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Diversity (politics); Epidemiology; Coronavirus Infections; Betacoronavirus; Data science; Geography; Virology; Medicine; Political science; Computer science; Outbreak; Pathology; Infectious disease (medical specialty)","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":[0.04648591,0.0005090946,0.00106932,0.00289304,0.001872846,0.005990427,0.002154033,0.002730294,0.004961984],"category_scores_gemma":[0.139063,0.0007033659,0.001295635,0.003969483,0.004839208,0.01376127,0.007148144,0.00630483,0.0006392883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003731906,"about_ca_system_score_gemma":0.00763823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03661897,"about_ca_topic_score_gemma":0.04167385,"domain_scores_codex":[0.9767451,0.01563073,0.001970073,0.001076588,0.002466747,0.002110774],"domain_scores_gemma":[0.9043312,0.07030638,0.007911273,0.004913037,0.008146722,0.004391398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0010626,0.0002842661,0.5772194,0.004725359,0.002322243,0.0006350054,0.125817,0.001274385,0.0009653522,0.03625977,0.06238566,0.1870491],"study_design_scores_gemma":[0.0002334439,0.0007095527,0.4536314,0.01215849,0.001036955,0.001302238,0.2354273,0.001877093,0.0007278681,0.1081739,0.1843507,0.0003711394],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2887996,0.05523626,0.006072863,0.6184806,0.00464038,0.0002112918,0.001812662,0.00005834528,0.02468797],"genre_scores_gemma":[0.8670856,0.02950508,0.004079266,0.09274054,0.003607036,0.0002888105,0.0006992726,0.0001317415,0.001862608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04648591,"threshold_uncertainty_score":0.2458439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4600161423313083,"score_gpt":0.4631741045270673,"score_spread":0.003157962195759034,"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."}}