{"id":"W4364353327","doi":"10.1007/s11538-023-01145-4","title":"PrEP Intervention in the Mitigation of HIV/AIDS Epidemics in China via a Data-Validated Age-Structured Model","year":2023,"lang":"en","type":"article","venue":"Bulletin of Mathematical Biology","topic":"HIV/AIDS Research and Interventions","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Markov chain Monte Carlo; Human immunodeficiency virus (HIV); Antiretroviral therapy; Intervention (counseling); Basic reproduction number; China; Pre-exposure prophylaxis; Medicine; Antiretroviral treatment; Epidemic model; Computer science; Viral load; Monte Carlo method; Virology; Statistics; Mathematics; Environmental health; Men who have sex with men; Population; Geography","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":[0.002734152,0.0008175374,0.001656948,0.0007346691,0.0005751024,0.001045128,0.00202902,0.001298954,0.003156113],"category_scores_gemma":[0.005161087,0.0004786279,0.001129001,0.0004377282,0.0008074586,0.001531758,0.0010545,0.001165267,0.0001783825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001749795,"about_ca_system_score_gemma":0.004928618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07308561,"about_ca_topic_score_gemma":0.03260727,"domain_scores_codex":[0.9992371,0.0003245856,0.00003244833,0.0001742286,0.00004619294,0.0001855588],"domain_scores_gemma":[0.9972076,0.001670691,0.0003443873,0.00010459,0.0004603298,0.0002124207],"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":[0.0002242181,0.0001848634,0.009550772,0.0001054635,0.0001162919,0.0001953551,0.00008379664,0.9749159,0.0001965056,0.00887833,0.0006666999,0.004881894],"study_design_scores_gemma":[0.00004954459,0.00006921323,0.001562462,0.00001028741,0.00008901033,0.00001297135,0.00003244129,0.9955907,0.00005885142,0.002384359,0.0001285673,0.00001149191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183549,0.001104708,0.07116961,0.002058547,0.00009933997,0.0002197961,0.001630881,0.000213156,0.005149158],"genre_scores_gemma":[0.9941617,0.0003053225,0.003021233,0.00008784118,0.00002635125,0.00009687775,0.0003288525,0.00001168962,0.001960052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07308561,"threshold_uncertainty_score":0.1453204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04999764714588204,"score_gpt":0.3695692505346434,"score_spread":0.3195716033887613,"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."}}