{"id":"W4328050300","doi":"10.5705/ss.202020.0318","title":"Hypothesis Test on a Mixture Forward-Incubation-Time Epidemic Model With Application to COVID-19 Outbreak","year":2023,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Higher Education Discipline Innovation Project; East China Normal University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Incubation period; Statistics; Coronavirus disease 2019 (COVID-19); Outbreak; Incubation; Mathematics; Identifiability; Time point; Likelihood-ratio test; Mixture model; Econometrics; Disease; Medicine; Biology; Virology; Infectious disease (medical specialty)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001496881,0.0003944943,0.0007997349,0.0001796913,0.0003713784,0.00003452588,0.0004222166,0.0001746193,0.0001348528],"category_scores_gemma":[0.103702,0.000291784,0.0001083032,0.00081315,0.0001858353,0.00005256089,0.0002030008,0.0002631964,0.002035998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003949422,"about_ca_system_score_gemma":0.0002176709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000674398,"about_ca_topic_score_gemma":0.0001309246,"domain_scores_codex":[0.9969662,0.0002171087,0.0007728249,0.0009128295,0.000504035,0.0006270015],"domain_scores_gemma":[0.9451957,0.05302886,0.0003036844,0.0008399786,0.0001493573,0.0004823862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005035992,0.0005283118,0.003351136,0.000590111,0.0002427947,0.00004024626,0.00125011,0.02173104,0.0005160767,0.187878,0.7709452,0.01242332],"study_design_scores_gemma":[0.0006782854,0.0005995001,0.00387879,0.00007671703,0.0001713273,0.000004017101,0.0000807857,0.1308071,0.00002418678,0.84922,0.01388261,0.0005766272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003895602,0.0000135233,0.9458108,0.04245726,0.00003442967,0.001797513,0.001668028,0.001151504,0.00317133],"genre_scores_gemma":[0.6314908,0.0000767299,0.3361704,0.0254384,0.0002076902,0.001869185,0.0001751394,0.0001742216,0.004397381],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7570626,"threshold_uncertainty_score":0.9999534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1769253859928497,"score_gpt":0.4235890909742764,"score_spread":0.2466637049814267,"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."}}