{"id":"W4205762838","doi":"10.1016/j.epidem.2022.100537","title":"A semi-parametric mixed model for short-term projection of daily COVID-19 incidence in Canada","year":2022,"lang":"en","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"Public Health Agency of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Parametric statistics; Term (time); Semiparametric model; Logistic regression; Pandemic; Econometrics; Projection (relational algebra); Parametric model; Covariate; Public health; Statistics; Computer science; Geography; Medicine; Mathematics","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":[0.003299501,0.001201162,0.001270272,0.001134562,0.0009998479,0.001943832,0.003458245,0.001562103,0.003763286],"category_scores_gemma":[0.0079159,0.0007508937,0.001315985,0.001439797,0.001596714,0.0007716336,0.00155095,0.00203934,0.0005685266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006679139,"about_ca_system_score_gemma":0.008536671,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6127725,"about_ca_topic_score_gemma":0.4411608,"domain_scores_codex":[0.9986446,0.000582114,0.00005145074,0.0002788452,0.0001588044,0.0002841834],"domain_scores_gemma":[0.995571,0.002655337,0.0004528465,0.0001445332,0.001015433,0.0001608449],"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":[0.0001681582,0.00003165298,0.007503606,0.00005765402,0.00008933044,0.0001615868,0.0001361375,0.9636026,0.0002337149,0.01785974,0.002014189,0.008141603],"study_design_scores_gemma":[0.00001414021,0.0000219814,0.001288133,0.00001194417,0.00002131213,0.00001496619,0.00003510435,0.9953506,0.00005617126,0.002446598,0.0007217274,0.00001719518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3386213,0.002296104,0.6354719,0.003860383,0.0002913154,0.000432978,0.009903582,0.0009333752,0.008189018],"genre_scores_gemma":[0.9406582,0.001026551,0.03871106,0.0002669714,0.0001044835,0.0005293463,0.003951643,0.0000891548,0.01466258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3872275,"threshold_uncertainty_score":0.7790159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3169514102245446,"score_gpt":0.4284084297991478,"score_spread":0.1114570195746032,"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."}}