{"id":"W3038410574","doi":"10.1101/2020.06.30.20143636","title":"Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Directorate for Biological Sciences; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Royal Society; Government of Canada; Ministry of Colleges and Universities","keywords":"Population; Geography; Pandemic; Social distance; Demography; Psychological intervention; Outbreak; Metropolitan area; Herd immunity; Population density; Intervention (counseling); Development economics; Coronavirus disease 2019 (COVID-19); Medicine; Economics; Disease; Virology; Sociology","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"],"consensus_categories":[],"category_scores_codex":[0.007618743,0.0002808103,0.0008585008,0.0002117168,0.0001144768,0.0000126238,0.0005120886,0.0002409934,0.000002711812],"category_scores_gemma":[0.02181666,0.0001475126,0.0002526937,0.0006999472,0.0005983332,0.00003009546,0.001018432,0.0007038368,2.123474e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207478,"about_ca_system_score_gemma":0.00002069245,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01127157,"about_ca_topic_score_gemma":0.02582435,"domain_scores_codex":[0.9947616,0.002935736,0.001282206,0.0003872815,0.000396123,0.0002370638],"domain_scores_gemma":[0.9785945,0.01952214,0.001211875,0.0005185461,0.0001132838,0.00003958156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001473517,0.0001909402,0.9809058,0.007578226,0.00006598141,0.00001610939,0.004574808,0.0009300644,0.00001020271,0.00504023,0.0002890505,0.0002512739],"study_design_scores_gemma":[0.0004969425,0.0001567285,0.8705184,0.000734292,0.0001723323,0.000002674911,0.002585991,0.006336629,0.00001341048,0.1188107,0.00004608851,0.0001258073],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845954,0.0003300534,0.009372027,0.004114608,0.00007432987,0.001417337,0.00007036646,0.00002151297,0.000004340519],"genre_scores_gemma":[0.9977328,0.001129274,0.0002857865,0.0005678057,0.00002381181,0.00006467401,0.0001817112,0.00001378346,3.977224e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1137704,"threshold_uncertainty_score":0.9953125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.15744091977814,"score_gpt":0.381176399255813,"score_spread":0.2237354794776731,"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."}}