{"id":"W3004905898","doi":"10.1101/2020.01.30.20019877","title":"Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Outbreak; Basic reproduction number; Interval (graph theory); Generation time; Coronavirus disease 2019 (COVID-19); Range (aeronautics); Population; Prediction interval; Statistics; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus; Econometrics; Estimation; Biology; Computer science; Demography; Infectious disease (medical specialty); Mathematics; Virology; Disease; Medicine; Economics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001617074,0.0004738152,0.0009958355,0.00003084877,0.0003333856,0.00005633376,0.0007675568,0.0003242403,0.00001032157],"category_scores_gemma":[0.02384872,0.0002741838,0.0001837753,0.0003247921,0.0003696167,0.00003679682,0.003314889,0.001095174,0.00002589476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042233,"about_ca_system_score_gemma":0.00008345327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005483372,"about_ca_topic_score_gemma":0.0001495964,"domain_scores_codex":[0.9970757,0.0001891056,0.000784767,0.001265945,0.0003350764,0.0003493961],"domain_scores_gemma":[0.9907671,0.006872829,0.0006207419,0.001369045,0.0002804588,0.00008978581],"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":[0.0004814779,0.000734398,0.6816875,0.005091924,0.002838046,0.00001109466,0.03043418,0.002344063,0.0217797,0.2357834,0.004296244,0.01451793],"study_design_scores_gemma":[0.0003246051,0.0000731282,0.178855,0.0009463224,0.0006523471,0.00001685205,0.0004728329,0.002192777,0.008241569,0.7973804,0.0100406,0.0008035992],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9562649,0.001054981,0.01610519,0.02258617,0.0002893964,0.002897721,0.000290995,0.0001505155,0.0003601137],"genre_scores_gemma":[0.9859362,0.000196126,0.01057766,0.001899324,0.0003846425,0.0008994243,0.000003948173,0.00005614193,0.00004658195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.561597,"threshold_uncertainty_score":0.999971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2842616360666432,"score_gpt":0.4302195776733477,"score_spread":0.1459579416067045,"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."}}