{"id":"W2731827591","doi":"10.1371/journal.pntd.0005696","title":"Bayesian dynamic modeling of time series of dengue disease case counts","year":2017,"lang":"en","type":"article","venue":"PLoS neglected tropical diseases","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Consejo Superior de Investigaciones Científicas; European Regional Development Fund; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Craft Ontario","keywords":"Deviance information criterion; Statistics; Mathematics; Poisson distribution; Random walk; Count data; Markov chain Monte Carlo; Bayesian probability; Quantile; Statistic; Bayesian inference; Deviance (statistics); Random effects model; Time series; Econometrics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000002317417,0.0001654566,0.0004122545,0.00006348618,0.0001300559,0.00002649368,0.000158961,0.00005450272,0.0007316579],"category_scores_gemma":[0.0006245744,0.0001414675,0.0002237621,0.00005873634,0.0001626324,0.0001127615,0.00005759728,0.00008501978,0.00002897889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002749935,"about_ca_system_score_gemma":0.0002008897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003397961,"about_ca_topic_score_gemma":0.000008941134,"domain_scores_codex":[0.9989026,0.00003670306,0.0002993859,0.0002506226,0.0002903213,0.0002203873],"domain_scores_gemma":[0.9984855,0.00003842961,0.0001394415,0.0006872262,0.0002065037,0.0004429058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.04345597,0.03073629,0.572606,0.01884101,0.008568315,0.05892067,0.0005171915,0.002683772,0.1940608,0.0117211,0.007318688,0.05057016],"study_design_scores_gemma":[0.00267276,0.0002937001,0.5239814,0.0006284066,0.002764011,0.00007765214,0.00003545095,0.4679529,0.0001803416,0.001039247,0.00006719362,0.0003069545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946875,0.002079609,0.0008456899,0.0003763493,0.00006417924,0.000427718,0.0007244408,0.00008144879,0.0007130895],"genre_scores_gemma":[0.9992041,0.00009315712,0.0001047871,0.0000590261,0.00009172418,0.00002774974,0.00007423942,0.00002878617,0.0003164213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4652691,"threshold_uncertainty_score":0.8011141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126616864448264,"score_gpt":0.2567390407296624,"score_spread":0.2454728720851797,"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."}}