{"id":"W2761866945","doi":"10.2196/publichealth.7376","title":"Lessons From the Implementation of Mo-Buzz, a Mobile Pandemic Surveillance System for Dengue","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Colombo; National Research Foundation","keywords":"Dengue fever; Pandemic; Developing country; Outreach; Public health; Outbreak; Environmental health; Sri lanka; Medicine; Disease surveillance; Geography; Socioeconomics; Infectious disease (medical specialty); Economic growth; Coronavirus disease 2019 (COVID-19); Disease; Virology","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":[0.009017121,0.0006497317,0.0002600404,0.0005246144,0.001830808,0.002695112,0.002073735,0.003321719,0.003376086],"category_scores_gemma":[0.02656825,0.000334365,0.0007866459,0.0003040203,0.001491833,0.004838356,0.002330904,0.003707725,0.001259918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446551,"about_ca_system_score_gemma":0.003521139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01485898,"about_ca_topic_score_gemma":0.02168051,"domain_scores_codex":[0.9953538,0.002795601,0.000215281,0.000311217,0.0007191851,0.0006049715],"domain_scores_gemma":[0.9894115,0.00507237,0.0004039769,0.0005450182,0.002771663,0.001795423],"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":[0.0006578446,0.001904984,0.08515783,0.002560878,0.0001125406,0.004466143,0.04797311,0.001890768,0.005913311,0.007161661,0.1785056,0.6636954],"study_design_scores_gemma":[0.0005603794,0.009315256,0.09742156,0.005398218,0.0003522451,0.006037967,0.1216446,0.009580362,0.009409642,0.007860108,0.7319199,0.0004996248],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4340719,0.006021539,0.03575242,0.4339152,0.00365686,0.003532801,0.001447451,0.003096857,0.07850502],"genre_scores_gemma":[0.8526827,0.005548342,0.06735716,0.05277146,0.0007895149,0.001462428,0.0009519858,0.0004086608,0.0180277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01485898,"threshold_uncertainty_score":0.04768771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05384890735594853,"score_gpt":0.3969275475096389,"score_spread":0.3430786401536903,"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."}}