{"id":"W2150154539","doi":"10.1007/s00285-014-0764-0","title":"An immuno-epidemiological model with threshold delay: a study of the effects of multiple exposures to a pathogen","year":2014,"lang":"en","type":"article","venue":"Journal of Mathematical Biology","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Delay differential equation; Pathogen; Basic reproduction number; Bistability; Epidemiology; Differential equation; Host (biology); Infectious disease (medical specialty); Transmission (telecommunications); Biology; Mathematical modelling of infectious disease; Mathematical model; Mathematics; Applied mathematics; Biological system; Control theory (sociology); Disease; Immunology; Computer science; Physics; Control (management); Medicine; Ecology; Statistics; Mathematical analysis; Telecommunications; Environmental health; Population; Artificial intelligence; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003931846,0.001421229,0.002989305,0.001328019,0.0009134458,0.002857406,0.003934245,0.00461111,0.005102098],"category_scores_gemma":[0.0137651,0.001057871,0.001636686,0.00121474,0.002380815,0.003402262,0.0022592,0.00308064,0.000347928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001636593,"about_ca_system_score_gemma":0.00204346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0115759,"about_ca_topic_score_gemma":0.00462603,"domain_scores_codex":[0.9987086,0.0006016903,0.00004127041,0.000203734,0.0001004405,0.0003442328],"domain_scores_gemma":[0.9850132,0.01062935,0.001708417,0.0004222756,0.0006376937,0.00158907],"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":[0.0008664588,0.0009413631,0.009027296,0.0003961546,0.0006584094,0.002242606,0.0004856033,0.7388996,0.004650369,0.2328317,0.003343577,0.005656863],"study_design_scores_gemma":[0.0002688973,0.0002608567,0.00137826,0.00001872497,0.0002112118,0.0002159297,0.0001875413,0.9667021,0.0002023524,0.0300285,0.0004719644,0.00005367996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7867569,0.002739236,0.1894778,0.008107038,0.0005331757,0.0001714761,0.0008335345,0.000180054,0.01120072],"genre_scores_gemma":[0.9841377,0.0009279195,0.005350362,0.0003338486,0.0002566342,0.00007547846,0.0001129419,0.00003334688,0.008771656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0115759,"threshold_uncertainty_score":0.02301705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560534299188489,"score_gpt":0.3070906469058026,"score_spread":0.2814853039139177,"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."}}