{"id":"W4399352730","doi":"10.1101/2024.06.05.24308495","title":"Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metapopulation; Coronavirus disease 2019 (COVID-19); Kalman filter; Extended Kalman filter; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computer science; Geography; Econometrics; Mathematics; Artificial intelligence; Biology; Medicine; Virology; Outbreak; Demography; Sociology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009345119,0.0002906476,0.000382932,0.0004971995,0.0004076145,0.0008171152,0.0006817283,0.0007848783,0.00183108],"category_scores_gemma":[0.002876392,0.0004015298,0.0007273988,0.0004746655,0.0004648939,0.0008021543,0.0008153085,0.0006212972,0.0001857911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001498392,"about_ca_system_score_gemma":0.001448105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1080785,"about_ca_topic_score_gemma":0.05333353,"domain_scores_codex":[0.9996811,0.0001475943,0.0000126764,0.00009412607,0.00003053283,0.00003399148],"domain_scores_gemma":[0.9991238,0.0005371284,0.0001560801,0.00004987585,0.00008854564,0.00004477934],"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":[0.000008930287,0.000006642467,0.00432247,0.000008150163,0.00002582694,0.00004367574,0.00004367357,0.9819595,0.0001523674,0.01034376,0.0004216801,0.002663249],"study_design_scores_gemma":[0.00000478421,0.000004503557,0.001027253,0.000004761887,0.000007386321,0.00001142817,0.00002430146,0.9907156,0.00005076958,0.007523228,0.0006211655,0.000004846253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3860851,0.0005067319,0.5987589,0.002486095,0.00007194451,0.00006131888,0.001290611,0.0003459721,0.01039336],"genre_scores_gemma":[0.9233534,0.0003256188,0.06926154,0.0001312744,0.00004966971,0.0001039063,0.0005040936,0.00005687897,0.006213723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1080785,"threshold_uncertainty_score":0.2148989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3036596626781609,"score_gpt":0.4000634633131322,"score_spread":0.09640380063497134,"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."}}