{"id":"W4406828882","doi":"10.1016/j.epidem.2025.100818","title":"Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter","year":2025,"lang":"en","type":"article","venue":"Epidemics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Centers for Disease Control and Prevention; National Institutes of Health","keywords":"Coronavirus disease 2019 (COVID-19); Metapopulation; 2019-20 coronavirus outbreak; Kalman filter; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Betacoronavirus; Extended Kalman filter; Biology; Virology; Computational biology; Computer science; Geography; Medicine; Outbreak; Artificial intelligence; Internal medicine; Disease; Environmental health; Population; Infectious disease (medical specialty)","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":[0.0008179332,0.0004557302,0.0005237428,0.0005587208,0.0006660812,0.0008895369,0.0009291786,0.001037937,0.001369859],"category_scores_gemma":[0.002044467,0.0005651499,0.001029289,0.0007063625,0.0004949315,0.0009467148,0.0008799147,0.0008487684,0.0001883386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001710394,"about_ca_system_score_gemma":0.002027198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2099189,"about_ca_topic_score_gemma":0.131758,"domain_scores_codex":[0.9996467,0.0001313973,0.00001791348,0.0001193905,0.00003385527,0.00005076843],"domain_scores_gemma":[0.999382,0.0002796766,0.0001400307,0.00003763319,0.0001132545,0.00004736592],"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":[0.00001070089,0.00001091738,0.006239302,0.000009125803,0.00003852037,0.00005198622,0.00004777043,0.9874045,0.0002126335,0.003173909,0.0003016214,0.002499036],"study_design_scores_gemma":[0.000004241001,0.000005533492,0.001165608,0.000003967376,0.00001312957,0.00000948587,0.00002063882,0.9971783,0.00003853391,0.001176188,0.0003768553,0.000007455315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.599236,0.0007226182,0.3870395,0.001716777,0.0001254073,0.00008567644,0.00186005,0.0004224732,0.008791521],"genre_scores_gemma":[0.9527269,0.0003492682,0.04205808,0.0001269171,0.00002991707,0.0001123965,0.000655523,0.00004017045,0.003900856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2099189,"threshold_uncertainty_score":0.417394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2739887680161358,"score_gpt":0.4102150540624363,"score_spread":0.1362262860463005,"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."}}