{"id":"W4281551947","doi":"10.1016/j.epidem.2022.100583","title":"Modeling waning and boosting of COVID-19 in Canada with vaccination","year":2022,"lang":"en","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Sleep Foundation; Hungarian Scientific Research Fund; Institute of Population and Public Health; James Merrill House; California State University, Northridge; Centers for Disease Control and Prevention; American Institute of Mathematics; National Science Foundation","keywords":"Vaccination; Herd immunity; Pandemic; Coronavirus disease 2019 (COVID-19); Medicine; Context (archaeology); Public health; Population; Environmental health; Social distance; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Immunology; Virology; Infectious disease (medical specialty); Biology; Disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001587835,0.001138928,0.001142637,0.001335845,0.001613602,0.001942163,0.002586877,0.001946683,0.005405833],"category_scores_gemma":[0.005482612,0.0007270402,0.001323327,0.001704365,0.001519463,0.0007711525,0.001394164,0.001479548,0.0003701404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03559322,"about_ca_system_score_gemma":0.0331769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9882161,"about_ca_topic_score_gemma":0.9734483,"domain_scores_codex":[0.9992167,0.0001642325,0.00002073955,0.0001177989,0.00007670777,0.0004038622],"domain_scores_gemma":[0.9974106,0.001016792,0.0003174247,0.00006325709,0.0007643375,0.0004276397],"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":[0.000285172,0.0001501811,0.04643136,0.00009127319,0.000124485,0.0002604752,0.0002787818,0.9265425,0.000441542,0.01618691,0.004045711,0.005161588],"study_design_scores_gemma":[0.0001675394,0.0001065384,0.01793201,0.00005688738,0.0001508671,0.00004066884,0.0004986188,0.9735372,0.0001566116,0.002977461,0.004319493,0.00005610903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378178,0.002591553,0.01318475,0.005873046,0.0001694475,0.0003277404,0.009994895,0.0002108147,0.02982991],"genre_scores_gemma":[0.9747532,0.001275577,0.004032302,0.0003459169,0.0000342039,0.0001232596,0.001870532,0.00003956492,0.01752551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03559322,"threshold_uncertainty_score":0.2582482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2362257613705684,"score_gpt":0.3924270771782572,"score_spread":0.1562013158076888,"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."}}