{"id":"W3102097060","doi":"10.1101/2020.11.11.20220962","title":"Short-term forecasts to inform the response to the Covid-19 epidemic in the UK","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; National Institute for Health Research Health Protection Research Unit; Department for International Development; University of Oxford; Imperial College London; Defence Science and Technology Laboratory; National Institute for Health and Care Research; Wellcome Trust; University of Massachusetts Amherst; Public Health England; Department of Health and Social Care; Biotechnology and Biological Sciences Research Council; Bill and Melinda Gates Foundation","keywords":"Quantile; Quantile regression; Econometrics; Statistics; Population; Prediction interval; Calibration; Regression; Term (time); Computer science; Null hypothesis; Mathematics; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001900211,0.0004110599,0.0003308816,0.0006568131,0.0001334306,0.001056198,0.0004328621,0.000595268,0.001677497],"category_scores_gemma":[0.01005523,0.0002105301,0.0003600118,0.0005395372,0.0001604591,0.0008735912,0.0006901546,0.0007884132,0.0002775728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007839787,"about_ca_system_score_gemma":0.0004449009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02856685,"about_ca_topic_score_gemma":0.01487366,"domain_scores_codex":[0.999595,0.0001873938,0.00003356459,0.00006298848,0.00007497522,0.00004611739],"domain_scores_gemma":[0.9977054,0.001300025,0.0003145843,0.0001795511,0.000389873,0.0001105764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002238666,0.00003756949,0.06550567,0.00006508666,0.0001111661,0.00009864432,0.0001368602,0.9040725,0.001100669,0.001772273,0.004304668,0.02257096],"study_design_scores_gemma":[0.00001586905,0.00005090655,0.01949774,0.000040382,0.0000197487,0.00002539057,0.0001042596,0.9759673,0.0006471499,0.002111877,0.0014954,0.00002398394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377325,0.0008606355,0.04598229,0.00264509,0.0003069687,0.0000558643,0.005910398,0.0005050537,0.006001141],"genre_scores_gemma":[0.9916603,0.000154014,0.005921979,0.00005428131,0.00005514132,0.00001151833,0.001527711,0.00002343034,0.0005916525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02856685,"threshold_uncertainty_score":0.05680114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4420504636571462,"score_gpt":0.4739464309863856,"score_spread":0.03189596732923938,"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."}}