{"id":"W4313598873","doi":"10.1109/jbhi.2023.3234818","title":"Spatio-Temporal Clustering of Multi-Location Time Series to Model Seasonal Influenza Spread","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Time series; Proxy (statistics); Temporal database; Computer science; Geography; Cluster (spacecraft); Spatial analysis; Spatial ecology; Hierarchical clustering; Data mining; Spatial epidemiology; Outbreak; Artificial intelligence; Medicine; Machine learning; Epidemiology; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114661,0.0001085999,0.0004505052,0.0003051458,0.00005771393,0.00001407928,0.00009978743,0.00006905643,0.00001520287],"category_scores_gemma":[0.0001906722,0.00008469697,0.00005273716,0.0004102272,0.0001052512,0.0002876718,0.00005737467,0.000180797,0.00003664801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000728139,"about_ca_system_score_gemma":0.0006801324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001520861,"about_ca_topic_score_gemma":0.00001024442,"domain_scores_codex":[0.9977642,0.00002396929,0.001323818,0.00005455537,0.0005973334,0.000236134],"domain_scores_gemma":[0.9982039,0.00003744264,0.0006307843,0.0001288059,0.000313561,0.0006854669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005909135,0.001573739,0.1174188,0.03395201,0.0008100225,0.0001591746,0.04010142,0.04173065,0.001174963,0.0001391568,0.3246309,0.4324001],"study_design_scores_gemma":[0.002861658,0.001335842,0.05548942,0.001593415,0.00003882136,0.0001943752,0.0004990103,0.9209551,0.00006574274,0.00006008034,0.01675109,0.0001554916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9128323,0.0002936355,0.07573038,0.009597418,0.0004523645,0.0005478789,0.0003941557,0.00008161718,0.00007025291],"genre_scores_gemma":[0.8325214,0.0008313441,0.1558968,0.00945015,0.000607356,0.000009899007,0.0003304897,0.00003957309,0.0003129009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8792244,"threshold_uncertainty_score":0.3453842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06185673431165625,"score_gpt":0.362287283472997,"score_spread":0.3004305491613407,"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."}}