{"id":"W4381955477","doi":"10.1016/b978-0-443-15299-3.00010-5","title":"Early warning for emerging infectious disease outbreaks: Digital disease surveillance for public health preparedness and response","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Public health; Preparedness; Social media; Disease surveillance; Infectious disease (medical specialty); Outbreak; Emerging infectious disease; Environmental health; Warning system; Public health surveillance; The Internet; Pandemic; Disease; Internet privacy; Medicine; Computer science; Political science; Coronavirus disease 2019 (COVID-19); Virology; Pathology; World Wide Web","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":[0.001024403,0.0009669454,0.0004628939,0.001886316,0.0004151597,0.005458972,0.000807554,0.001857154,0.05913677],"category_scores_gemma":[0.00246373,0.0003739573,0.0003841101,0.002007948,0.0007217254,0.005126311,0.001808168,0.00181618,0.02698861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006718,"about_ca_system_score_gemma":0.001321331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002861723,"about_ca_topic_score_gemma":0.00561266,"domain_scores_codex":[0.9995597,0.00008489779,0.00002140291,0.00004895869,0.0002580674,0.00002692064],"domain_scores_gemma":[0.9984924,0.001086376,0.00004657803,0.00007374576,0.000213456,0.00008756165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000008565277,0.00002274347,0.000147832,0.000379024,0.00000816548,0.00004721756,0.0001405264,0.0006488495,0.0005475424,0.03672562,0.4439703,0.5173536],"study_design_scores_gemma":[0.00000229653,0.000007594246,0.0001828838,0.0004853627,0.000004440546,0.0001107742,0.0001027048,0.0007717286,0.0002011769,0.02027832,0.9778423,0.00001038502],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001062636,0.1517007,0.06136904,0.02285517,0.01205645,0.0001333252,0.001483359,0.002027511,0.7473119],"genre_scores_gemma":[0.01376942,0.1763572,0.05438619,0.01041606,0.007001465,0.0001390906,0.00157832,0.0008597559,0.7354925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05913677,"threshold_uncertainty_score":0.1978322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03306238311318024,"score_gpt":0.2952637057157309,"score_spread":0.2622013226025507,"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."}}