{"id":"W3098483725","doi":"10.3390/healthcare8040469","title":"A Drive-through Simulation Tool for Mass Vaccination during COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"Healthcare","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Durham; Response Biomedical (Canada); York University","funders":"Canadian Institutes of Health Research; Public Health Agency; Public Health Agency of Canada","keywords":"Vaccination; Pandemic; Immunization; Preparedness; Coronavirus disease 2019 (COVID-19); Mass vaccination; Computer science; Medical emergency; Medicine; Virology; Immunology; Political science; Disease","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065301,0.0006195038,0.0005374838,0.0005611166,0.0004408626,0.0007687297,0.001083711,0.001012098,0.008425254],"category_scores_gemma":[0.001804985,0.0003916526,0.0006663755,0.000343915,0.0002667023,0.0006430304,0.0007418917,0.0005283663,0.0005517054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006903269,"about_ca_system_score_gemma":0.001094953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01377966,"about_ca_topic_score_gemma":0.009660915,"domain_scores_codex":[0.9998116,0.00007848028,0.00001774195,0.00002571011,0.00004254189,0.0000238968],"domain_scores_gemma":[0.9988326,0.0008577093,0.00005358948,0.0000513091,0.0001391893,0.00006552158],"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":[0.0001171124,0.00009914371,0.002258116,0.00007209943,0.00002962764,0.0001053605,0.0001052643,0.9833993,0.001192069,0.003586435,0.001635743,0.007399701],"study_design_scores_gemma":[0.00002925507,0.00002309425,0.000178508,0.000007916384,0.000006782081,0.00001233682,0.00002035415,0.9970246,0.0004289723,0.0006525707,0.001608683,0.000006983027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3002596,0.0002369119,0.6415816,0.0007340673,0.0002033483,0.0006218044,0.006562815,0.01468749,0.03511232],"genre_scores_gemma":[0.8140635,0.0003315664,0.1746635,0.000132139,0.00002312371,0.0007025658,0.003006995,0.0006061115,0.006470448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01377966,"threshold_uncertainty_score":0.02818531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5080193349386872,"score_gpt":0.5206454082899811,"score_spread":0.01262607335129384,"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."}}