{"id":"W2528106982","doi":"10.1101/076521","title":"Dynamic forecasting of Zika epidemics using Google Trends","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive integrated moving average; Zika virus; Autoregressive model; Outbreak; Warning system; Computer science; Time series; Data mining; Statistics; Geography; Medicine; Machine learning; Virology; Mathematics; Virus; Telecommunications","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":[0.0009133506,0.000444535,0.0003701307,0.001486812,0.0001521836,0.0008403636,0.0006036593,0.0004370375,0.00110012],"category_scores_gemma":[0.003621259,0.0002139451,0.0006490521,0.001278055,0.0001483134,0.00110972,0.0003396794,0.0004927802,0.0003914163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007695323,"about_ca_system_score_gemma":0.000562685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06837182,"about_ca_topic_score_gemma":0.03122432,"domain_scores_codex":[0.9997258,0.00007224677,0.00002254605,0.0000779793,0.00006328853,0.00003823635],"domain_scores_gemma":[0.999222,0.0003540356,0.0001591332,0.00005717924,0.0001670701,0.00004048449],"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":[0.0001607574,0.0000789483,0.1696733,0.00008532864,0.0001484152,0.0001562892,0.0001063991,0.7923306,0.001180938,0.003994213,0.002229285,0.02985569],"study_design_scores_gemma":[0.000003556459,0.00001335973,0.008279688,0.000006960964,0.00001082584,0.00001287914,0.00002031267,0.9905995,0.0001381336,0.0004951105,0.0004136495,0.000005990537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495206,0.0004777386,0.04032658,0.0008186802,0.00007380542,0.000060906,0.00460334,0.0006673703,0.003450979],"genre_scores_gemma":[0.9906064,0.0001687972,0.006513496,0.00002370181,0.00002215795,0.00001823931,0.002105442,0.00001743799,0.0005243161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06837182,"threshold_uncertainty_score":0.1359477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0328025770339365,"score_gpt":0.2722828959833138,"score_spread":0.2394803189493774,"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."}}