{"id":"W4310457196","doi":"10.2196/40825","title":"State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Operationalization; Politics; Lag; State (computer science); Coronavirus disease 2019 (COVID-19); Pandemic; Demography; Political science; Demographic economics; Psychology; Statistics; Medicine; Disease; Economics; Computer science; Mathematics; Sociology; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006457099,0.0002545211,0.0003987896,0.003424884,0.0005642787,0.001918198,0.0006733811,0.000611248,0.002781454],"category_scores_gemma":[0.04994446,0.0002689686,0.0009456474,0.007341745,0.0007615975,0.002365507,0.001532516,0.001345471,0.0007174525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007964332,"about_ca_system_score_gemma":0.0009968167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630484,"about_ca_topic_score_gemma":0.01834031,"domain_scores_codex":[0.9951004,0.001864334,0.0005676627,0.0009980389,0.0009820716,0.000487525],"domain_scores_gemma":[0.9331877,0.04272811,0.01636452,0.003051884,0.00333635,0.001331442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008957268,0.00005460137,0.9928665,0.00006453838,0.0001010433,0.00003676294,0.0009177623,0.0006920086,0.0001171965,0.000444982,0.001237016,0.003378002],"study_design_scores_gemma":[0.000009237971,0.0001095472,0.9830512,0.00007065434,0.00006095462,0.0001235838,0.004087151,0.009293016,0.0002619308,0.0006777452,0.002222913,0.00003200124],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814627,0.0002637152,0.00203387,0.0005531333,0.00002622942,0.0001037684,0.01375108,0.0000658053,0.001739693],"genre_scores_gemma":[0.9921525,0.00006244061,0.001151353,0.00005262742,0.00001796842,0.000151111,0.006111631,0.00001578746,0.0002846059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01630484,"threshold_uncertainty_score":0.03414887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2514002726675043,"score_gpt":0.509529806523304,"score_spread":0.2581295338557997,"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."}}