{"id":"W3033386675","doi":"10.1016/j.mbs.2020.108391","title":"A data-driven network model for the emerging COVID-19 epidemics in Wuhan, Toronto and Italy","year":2020,"lang":"en","type":"article","venue":"Mathematical Biosciences","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Canadian Institutes of Health Research; La Trobe University; York University; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Pandemic; Megacity; Markov chain Monte Carlo; Coronavirus disease 2019 (COVID-19); Transmission (telecommunications); Public health; China; Geography; Markov chain; Node (physics); Epidemic model; Computer science; Environmental health; Medicine; Disease; Engineering; Telecommunications; Infectious disease (medical specialty); Population; Bayesian probability; Economics; Machine learning; Artificial intelligence","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":[0.0013386,0.0006600209,0.0008751166,0.001011147,0.0007753726,0.001735041,0.002220615,0.001878963,0.004473327],"category_scores_gemma":[0.005265445,0.0004467708,0.000649044,0.001009813,0.001268259,0.001116352,0.001354746,0.001441808,0.0004314953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007181476,"about_ca_system_score_gemma":0.003550277,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.360099,"about_ca_topic_score_gemma":0.2449498,"domain_scores_codex":[0.9995769,0.0001794132,0.0000132124,0.00008938503,0.0000385402,0.0001026453],"domain_scores_gemma":[0.9982277,0.0009567989,0.0002723876,0.00006925157,0.0002584584,0.0002154788],"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":[0.0001156951,0.00003934036,0.005367597,0.00007180449,0.00004979513,0.0003095829,0.0002210766,0.8747759,0.0003559928,0.1093543,0.006550699,0.002788288],"study_design_scores_gemma":[0.00003684697,0.00002152448,0.00146529,0.00001699022,0.00002206785,0.00003189162,0.0001153591,0.9859955,0.00004408638,0.01032637,0.001906531,0.00001757654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7276435,0.002728892,0.1905036,0.02225237,0.0003953085,0.0003152792,0.01626647,0.0004971992,0.03939747],"genre_scores_gemma":[0.9728018,0.0008472721,0.006952542,0.0002259148,0.00007766228,0.0001542803,0.002063781,0.00003618505,0.01684048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.639901,"threshold_uncertainty_score":0.7160059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5615230966783253,"score_gpt":0.4884006212198701,"score_spread":0.07312247545845524,"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."}}