{"id":"W3183521648","doi":"10.1101/2021.07.22.453430","title":"Cross immunity protection and antibody dependent enhancement: A distributed delay dynamic model","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Dengue fever; Lyapunov function; Stability (learning theory); Basic reproduction number; Delay differential equation; Transmission (telecommunications); Bifurcation; Immunity; Epidemic model; Dynamics (music); Computer science; Control theory (sociology); Applied mathematics; Mathematics; Biology; Immunology; Differential equation; Physics; Immune system; Mathematical analysis; Telecommunications; Artificial intelligence; Medicine; Nonlinear system; Control (management)","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":[0.0005064472,0.0007629992,0.0009000264,0.0005538115,0.0003978238,0.001231691,0.001284442,0.001925805,0.002993109],"category_scores_gemma":[0.001069259,0.0003413382,0.0007623789,0.0004224074,0.0008750319,0.0009491886,0.001047034,0.0009419202,0.0002570201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009126302,"about_ca_system_score_gemma":0.0006669727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004247198,"about_ca_topic_score_gemma":0.001940647,"domain_scores_codex":[0.9997861,0.00006473604,0.0000106286,0.00006011162,0.00003547949,0.00004294423],"domain_scores_gemma":[0.9994931,0.0002113778,0.0001477893,0.0000241925,0.00006442898,0.00005909862],"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":[0.00009499086,0.0001205026,0.00238792,0.00009922521,0.00007699302,0.0005725014,0.0001770315,0.9120698,0.00813329,0.07255065,0.0006433603,0.003073789],"study_design_scores_gemma":[0.00002514027,0.00004105299,0.0002496344,0.000005980958,0.00001930896,0.00004430369,0.00001871955,0.9938508,0.000208614,0.005150139,0.0003770324,0.000009247513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4524523,0.00126128,0.5077657,0.002761727,0.0003185573,0.0001778737,0.0007216862,0.0001980066,0.03434301],"genre_scores_gemma":[0.9811183,0.0003980746,0.006885142,0.00009843703,0.00005760682,0.0001066111,0.00007274868,0.00001493033,0.01124807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004247198,"threshold_uncertainty_score":0.01001292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964197955239147,"score_gpt":0.2824547342570019,"score_spread":0.2628127547046104,"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."}}