{"id":"W3024128873","doi":"10.3390/biology9050100","title":"De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study","year":2020,"lang":"en","type":"article","venue":"Biology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; York University","funders":"Canadian Institutes of Health Research; Gruppo Nazionale per il Calcolo Scientifico; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Quarantine; Contact tracing; Social distance; Coronavirus disease 2019 (COVID-19); Pandemic; Isolation (microbiology); Biology; Transmission (telecommunications); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Reversing; Social contact; Basic reproduction number; Demography; Virology; Social psychology; Computer science; Psychology; Engineering; Disease; Infectious disease (medical specialty); Medicine; Ecology; Telecommunications; Internal medicine; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001092097,0.0001239197,0.0003712889,0.00002578285,0.0001521608,0.000006752151,0.00004781792,0.00007653932,0.00003488339],"category_scores_gemma":[0.004544731,0.00008443281,0.0000311405,0.0001414473,0.0001010991,0.00004330983,0.00003646427,0.0001999749,5.498184e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004681482,"about_ca_system_score_gemma":0.0003956076,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8801966,"about_ca_topic_score_gemma":0.9484304,"domain_scores_codex":[0.998319,0.0006754679,0.0004626376,0.0002847686,0.0001018169,0.0001563046],"domain_scores_gemma":[0.9977157,0.001653999,0.0004068678,0.000104455,0.00006138772,0.00005756892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004682218,0.0001326158,0.9862072,0.0002096958,0.00006397246,0.00002627481,0.009868835,0.00005998968,0.002524591,0.0002141106,0.00004629715,0.0001781189],"study_design_scores_gemma":[0.002873252,0.002655688,0.9635412,0.0001162272,0.000239295,0.0001277089,0.01516235,0.01240782,0.0001131946,0.002393688,0.00008819361,0.0002813824],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748069,0.00005170795,0.02287227,0.001037264,0.0000124996,0.001171319,0.00001392544,0.00001209992,0.00002203589],"genre_scores_gemma":[0.9994402,7.230572e-7,0.0003856536,0.0001074978,0.00001359442,0.00003318186,0.000004902327,0.000005462549,0.000008806674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0682338,"threshold_uncertainty_score":0.5440795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1858221970175698,"score_gpt":0.3793912179583889,"score_spread":0.1935690209408192,"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."}}