{"id":"W3145677695","doi":"10.1016/s1473-3099(21)00057-8","title":"Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study","year":2021,"lang":"en","type":"article","venue":"The Lancet Infectious Diseases","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":231,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Colleges and Universities; Ministry of Training, Colleges and Universities","keywords":"Epidemiology; Vaccination; Psychological intervention; Medicine; Population; Pandemic; Transmission (telecommunications); Demography; Disease; Social distance; Social epidemiology; Environmental health; Coronavirus disease 2019 (COVID-19); Social determinants of health; Public health; Immunology; Infectious disease (medical specialty); Pathology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002192051,0.0002268785,0.0008899512,0.0001076642,0.0005789924,0.00006664856,0.0001412996,0.00009264968,0.00009029296],"category_scores_gemma":[0.01828096,0.0001491951,0.0001129084,0.000514934,0.00006250322,0.00007985478,0.0003270832,0.0002864802,0.000005834803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002090457,"about_ca_system_score_gemma":0.00006130955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004794572,"about_ca_topic_score_gemma":0.0002000509,"domain_scores_codex":[0.9972119,0.001165263,0.0004909589,0.0004277088,0.0002040955,0.0005000609],"domain_scores_gemma":[0.9900501,0.009372787,0.0001593547,0.0002407994,0.00005932578,0.0001175848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001026194,0.001436537,0.8583508,0.0007584345,0.0001754971,0.0001923813,0.007594824,0.004048519,0.000007025931,0.1241584,0.0008808492,0.002294146],"study_design_scores_gemma":[0.001827749,0.00009920928,0.08382392,0.00006405471,0.0001697025,0.00003708386,0.003006493,0.02258522,0.000001407366,0.8878071,0.0002843433,0.0002937507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462077,0.0008950409,0.04614571,0.005015143,0.00005426382,0.0006426008,0.00001896994,0.0003623959,0.0006581956],"genre_scores_gemma":[0.9966006,0.0002300983,0.0004779762,0.002073338,0.0004125399,0.0001615224,0.000005137286,0.00001663756,0.00002212682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7745268,"threshold_uncertainty_score":0.9899885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.274556366011219,"score_gpt":0.4484468299967159,"score_spread":0.1738904639854969,"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."}}