{"id":"W3014628651","doi":"10.1101/2020.04.01.20049973","title":"Modeling risk of infectious diseases: a case of Coronavirus outbreak in four countries","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Outbreak; China; Geography; Transmission (telecommunications); Coronavirus; Population; Pandemic; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Socioeconomics; Infectious disease (medical specialty); Demography; Environmental health; Disease; Medicine; Virology; Engineering; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001079978,0.0003730245,0.001627461,0.000123723,0.00005668011,0.00001013157,0.0003136794,0.0002755432,0.00003783777],"category_scores_gemma":[0.02072827,0.0003065551,0.0003538066,0.0001777528,0.0001867247,0.00003152667,0.001367496,0.0006848876,0.00000609958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001638595,"about_ca_system_score_gemma":0.0001432497,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007271046,"about_ca_topic_score_gemma":0.00265842,"domain_scores_codex":[0.9971012,0.0005114187,0.001272949,0.0005597624,0.0002743844,0.0002802396],"domain_scores_gemma":[0.9943311,0.003947675,0.000798203,0.0006157195,0.000200458,0.0001068942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001308236,0.0002049509,0.9517227,0.003819926,0.0003757217,0.0007413461,0.001661228,0.03701959,0.00001243803,0.003786582,0.00004993276,0.000474735],"study_design_scores_gemma":[0.001187627,0.0001770788,0.04159173,0.0009177324,0.0009573561,0.00004708403,0.0003652819,0.1901202,0.00004437482,0.7638255,0.0001101828,0.0006558774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789696,0.001346209,0.01775077,0.0002306734,0.0001707285,0.0006601522,0.0006117811,0.0001147547,0.0001453827],"genre_scores_gemma":[0.9973807,0.001106295,0.001174682,0.000104077,0.00007200792,0.0001157929,0.00000511506,0.00003480547,0.000006469852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.910131,"threshold_uncertainty_score":0.9999387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2194440474176049,"score_gpt":0.4125196616982846,"score_spread":0.1930756142806797,"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."}}