{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001480963,0.0004811534,0.0003902778,0.0004472313,0.001161187,0.0006981349,0.001440176,0.000459577,0.0008572842],"category_scores_gemma":[0.00350152,0.0001427708,0.0003860006,0.0004229312,0.0007453465,0.0003510358,0.0006395918,0.0005050426,0.00006939466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01656623,"about_ca_system_score_gemma":0.01457005,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9166531,"about_ca_topic_score_gemma":0.9370342,"domain_scores_codex":[0.9990068,0.0002776011,0.00003166414,0.0001127874,0.0002005561,0.0003706168],"domain_scores_gemma":[0.9989317,0.0002921156,0.0001707485,0.00008040415,0.0003642139,0.0001607317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004488986,0.006901833,0.5189308,0.001564782,0.0008555141,0.003075205,0.007547169,0.1438188,0.06091071,0.01352215,0.005365225,0.2330189],"study_design_scores_gemma":[0.0008561641,0.01080461,0.7915956,0.0003120843,0.00083463,0.0004956832,0.01093513,0.1569682,0.01158345,0.002892489,0.01255901,0.000162843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965491,0.0002073214,0.0005246381,0.0002395209,0.000004431495,0.0001831749,0.00009238729,0.000009426065,0.002189926],"genre_scores_gemma":[0.9980262,0.0001586664,0.001080752,0.00004162729,0.000002837261,0.00004542289,0.00005836343,0.000001180334,0.0005849432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0833469,"threshold_uncertainty_score":0.1676754,"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."}}