{"id":"W2171187415","doi":"10.1007/s11538-007-9257-2","title":"A Delay Differential Model for Pandemic Influenza with Antiviral Treatment","year":2007,"lang":"en","type":"article","venue":"Bulletin of Mathematical Biology","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Winnipeg; National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs; Ontario Ministry of Health and Long-Term Care; Coral Reef Conservation Program; Hungarian Scientific Research Fund; Public Health Agency; Public Health Agency of Canada","keywords":"Pandemic; Influenza pandemic; Disease; Medicine; Disease control; Epidemic model; Antiviral treatment; Coronavirus disease 2019 (COVID-19); Intensive care medicine; Virology; Virus; Infectious disease (medical specialty); Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001483416,0.001505472,0.002643268,0.0009333947,0.00094975,0.002415598,0.002804888,0.004738275,0.008524874],"category_scores_gemma":[0.004334415,0.0007949069,0.001292784,0.0007914007,0.001746724,0.0019338,0.001743576,0.002666582,0.000677221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002874638,"about_ca_system_score_gemma":0.002242065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01574391,"about_ca_topic_score_gemma":0.007420445,"domain_scores_codex":[0.9994212,0.0001790079,0.00002267224,0.0001340859,0.00007653624,0.0001665385],"domain_scores_gemma":[0.9980896,0.001026636,0.0003081608,0.00005450968,0.0002032358,0.0003178899],"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":[0.0004654135,0.0001625378,0.002122085,0.0002622534,0.0001730299,0.001225449,0.0003263382,0.590745,0.003994745,0.3905069,0.005027585,0.004988676],"study_design_scores_gemma":[0.0002952982,0.0001758627,0.0005429498,0.00002206533,0.0001225227,0.0002213584,0.0001097733,0.9270719,0.0002304195,0.06917074,0.001983269,0.00005382895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3840272,0.004079884,0.5185531,0.02538474,0.001721897,0.0002849427,0.003909491,0.0004451486,0.06159353],"genre_scores_gemma":[0.9378222,0.001181199,0.007832297,0.0006201336,0.0003563472,0.0001618487,0.00038055,0.00005070661,0.05159471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01574391,"threshold_uncertainty_score":0.03130454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2243603655348784,"score_gpt":0.4248652204077248,"score_spread":0.2005048548728465,"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."}}