{"id":"W4307833053","doi":"10.1101/2022.10.27.22281524","title":"An <i>R</i> <sub> <i>t</i> </sub> - based model for predicting multiple epidemic waves in a heterogeneous population","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; George & Fay Yee Centre for Healthcare Innovation","funders":"Research Manitoba","keywords":"Homogeneous; Epidemic model; Pandemic; Mixing (physics); Coronavirus disease 2019 (COVID-19); Population; Computer science; Basic reproduction number; Network structure; Econometrics; Statistical physics; Geography; Demography; Mathematics; Physics; Theoretical computer science; Medicine; Sociology; 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":[0.001405038,0.0004498595,0.0006406756,0.0007785599,0.0004613088,0.001137654,0.001616742,0.001259545,0.002157638],"category_scores_gemma":[0.003935413,0.0003250932,0.0006639539,0.0006249439,0.0008254156,0.001162083,0.0006847512,0.001037695,0.0003619195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388805,"about_ca_system_score_gemma":0.0007712315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02010528,"about_ca_topic_score_gemma":0.01125105,"domain_scores_codex":[0.9997367,0.0001012732,0.00001179516,0.00007299866,0.00003065954,0.00004654601],"domain_scores_gemma":[0.99839,0.0009066784,0.0003142488,0.00009230849,0.0001802359,0.00011672],"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":[0.00002574677,0.00002646123,0.003086323,0.000008196484,0.00001487079,0.00004953273,0.00003097032,0.986492,0.0003643664,0.007520453,0.0004144162,0.001966602],"study_design_scores_gemma":[0.000002839395,0.000006797578,0.000265026,0.000001470448,0.000002156749,0.000005499474,0.000004566116,0.9977289,0.00002804834,0.001885626,0.00006686664,0.000002209641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.555359,0.0003753117,0.4317729,0.00295748,0.0001140096,0.000121519,0.00125687,0.0002824804,0.007760337],"genre_scores_gemma":[0.972892,0.0001820686,0.02053217,0.0001390083,0.0000752022,0.00008027272,0.0003386453,0.00003574376,0.005724921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02010528,"threshold_uncertainty_score":0.03997648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1998333415442694,"score_gpt":0.3933819649588216,"score_spread":0.1935486234145522,"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."}}