{"id":"W3174061332","doi":"10.1101/2021.06.22.21259346","title":"Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Centers for Disease Control and Prevention; National Science Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Autoregressive model; Pandemic; Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Econometrics; Predictive modelling; Hotspot (geology); Actuarial science; Business; Economics; Medicine; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008433668,0.001236189,0.001302108,0.002466882,0.0003803157,0.00313618,0.001507514,0.001412492,0.003809044],"category_scores_gemma":[0.04537459,0.0003810959,0.001213959,0.002009386,0.0006153742,0.003528304,0.001627912,0.002581999,0.001423643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008885894,"about_ca_system_score_gemma":0.001214477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01276838,"about_ca_topic_score_gemma":0.008373732,"domain_scores_codex":[0.9984675,0.0007949505,0.000100583,0.0003490092,0.0001283797,0.0001595446],"domain_scores_gemma":[0.9807283,0.01424351,0.001640359,0.001325825,0.001388264,0.0006737409],"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":[0.0009108476,0.000285641,0.2774686,0.0003084114,0.0004284205,0.0001784964,0.000231159,0.5513573,0.0008151389,0.01938877,0.01645509,0.1321722],"study_design_scores_gemma":[0.00003142119,0.00007726837,0.01301183,0.00007313536,0.00004354204,0.00002967475,0.0001087127,0.9664943,0.0003132914,0.01788248,0.00191023,0.00002411307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6285194,0.005389059,0.321174,0.01873439,0.001314772,0.0002023525,0.009481275,0.003033312,0.01215154],"genre_scores_gemma":[0.9662994,0.0006453499,0.02685107,0.0002993705,0.0005480794,0.00004053653,0.004189308,0.00009194552,0.001034942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01276838,"threshold_uncertainty_score":0.04460204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2225815925536732,"score_gpt":0.3971392468486366,"score_spread":0.1745576542949634,"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."}}