{"id":"W4282939334","doi":"10.2196/35343","title":"Changes in Temporal Properties of Notifiable Infectious Disease Epidemics in China During the COVID-19 Pandemic: Population-Based Surveillance Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health Commission of the People's Republic of China","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Virology; China; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Notifiable disease; Environmental health; Population; Disease surveillance; Outbreak; Medicine; Disease; Geography","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":[0.001621806,0.0005047683,0.0005554571,0.002337244,0.0004252765,0.0006833946,0.0007353994,0.0005315132,0.0008122412],"category_scores_gemma":[0.003011029,0.0003119876,0.0008427564,0.002196168,0.0003528235,0.0007323428,0.0006965973,0.000487497,0.0001453902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173218,"about_ca_system_score_gemma":0.001295684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04885363,"about_ca_topic_score_gemma":0.03934264,"domain_scores_codex":[0.9992718,0.000113808,0.000102579,0.0002631357,0.0001260379,0.0001226966],"domain_scores_gemma":[0.997443,0.0003351426,0.0009564977,0.0003386283,0.000551436,0.0003752904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003521923,0.00002069473,0.9973328,0.00001659421,0.00008915749,0.00005435015,0.000202886,0.0002879145,0.000213486,0.00003142643,0.0001935571,0.001522002],"study_design_scores_gemma":[0.000002404661,0.00002447948,0.9983491,0.000003442417,0.00002188341,0.00003741956,0.0001487481,0.001248193,0.00004169727,0.00001604129,0.0001014248,0.000005306114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980602,0.00008786722,0.0002137978,0.00004283589,0.000005442343,0.00002064807,0.001346258,0.000009794434,0.000213208],"genre_scores_gemma":[0.9974286,0.00006560637,0.0001873734,0.00002592134,0.00001159697,0.00003079234,0.002129837,0.000002926002,0.000117369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04885363,"threshold_uncertainty_score":0.09713852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2007741399077367,"score_gpt":0.400343328926164,"score_spread":0.1995691890184274,"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."}}