{"id":"W4404261018","doi":"10.48550/arxiv.2410.16617","title":"Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada First Research Excellence Fund; Fundação Oswaldo Cruz; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Escola Nacional de Saúde Pública Sérgio Arouca; Compute Canada; McGill University","keywords":"Multinomial distribution; Zero (linguistics); Markov chain; Infectious disease (medical specialty); Econometrics; Mathematics; Computer science; Medicine; Statistics; Disease; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05588716,0.00203762,0.003666273,0.004933559,0.001443147,0.003285446,0.006510022,0.004333765,0.01102389],"category_scores_gemma":[0.1710385,0.001375937,0.005453613,0.004960917,0.004085547,0.00569335,0.003980752,0.005709691,0.001184578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003471052,"about_ca_system_score_gemma":0.002313403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008789871,"about_ca_topic_score_gemma":0.00536293,"domain_scores_codex":[0.9562675,0.03591973,0.001161127,0.003976362,0.001584082,0.001091207],"domain_scores_gemma":[0.7163994,0.2517998,0.01413796,0.01291182,0.002909931,0.001841048],"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":[0.00112459,0.0003902545,0.03501364,0.0005328867,0.002329145,0.0009102348,0.00115986,0.5534283,0.001075595,0.3457833,0.00334491,0.05490719],"study_design_scores_gemma":[0.00008105142,0.0002389157,0.00324146,0.00008675701,0.0001800075,0.0001382615,0.0001067623,0.7870502,0.0002401135,0.2066527,0.00192035,0.00006335214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04168925,0.0008369955,0.9529296,0.0008311412,0.0002942747,0.0002280668,0.00117119,0.0006483888,0.001371054],"genre_scores_gemma":[0.6415401,0.001324365,0.3434125,0.0007630841,0.0006623343,0.002381332,0.003931189,0.0004289441,0.00555616],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05588716,"threshold_uncertainty_score":0.2955631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2465702463476232,"score_gpt":0.2894035701815017,"score_spread":0.04283332383387853,"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."}}