{"id":"W3086411451","doi":"10.2196/19589","title":"Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Outbreak; Social media; Coronavirus disease 2019 (COVID-19); Pandemic; Medicine; Index (typography); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); China; Demography; Virology; Geography; Internal medicine; Computer science; Disease; Sociology; World Wide Web","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":[0.001304438,0.0007106724,0.0006457379,0.00196424,0.001245748,0.00118904,0.0005878045,0.0007562927,0.002606763],"category_scores_gemma":[0.006223952,0.0005892442,0.0008467146,0.001566211,0.0004955981,0.001592696,0.001057497,0.001045844,0.001172118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000868462,"about_ca_system_score_gemma":0.001598902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02463172,"about_ca_topic_score_gemma":0.0213239,"domain_scores_codex":[0.9985693,0.000244939,0.0002304334,0.000347333,0.0003194327,0.0002886073],"domain_scores_gemma":[0.9960566,0.000798657,0.001106389,0.0002747547,0.00130899,0.0004545925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000285962,0.0002502719,0.9880258,0.0002214515,0.00008362896,0.000868756,0.001076478,0.00004455695,0.0002669061,0.00004454289,0.001933238,0.006898284],"study_design_scores_gemma":[0.00003511463,0.0007756763,0.9861921,0.0001503112,0.0003047827,0.002022677,0.003702225,0.00101881,0.0006011956,0.00007769168,0.005041585,0.00007793902],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918026,0.0007499048,0.0004628569,0.0001356686,0.00002720539,0.0003941195,0.004706995,0.0000380087,0.001682723],"genre_scores_gemma":[0.9925371,0.0006477059,0.0006097521,0.0002828658,0.00005511102,0.0004890581,0.004133414,0.00002347202,0.001221433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02463172,"threshold_uncertainty_score":0.04897666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07865378927646081,"score_gpt":0.4105371500938845,"score_spread":0.3318833608174237,"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."}}