{"id":"W3104139592","doi":"10.1007/s10489-020-01929-4","title":"SEIAQRDT model for the spread of novel coronavirus (COVID-19): A case study in India","year":2020,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Computer science; Quarantine; Isolation (microbiology); Contact tracing; Epidemic model; Transmission (telecommunications); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Basic reproduction number; Coronavirus; 2019-20 coronavirus outbreak; Asymptomatic; Control (management); Econometrics; Statistics; Operations research; Artificial intelligence; Virology; Outbreak; Environmental health; Medicine; Infectious disease (medical specialty); Biology; Mathematics; Bioinformatics; Telecommunications; Disease","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":[0.001453719,0.0004837105,0.001092784,0.001324215,0.000922388,0.002472681,0.00283054,0.002283396,0.008080973],"category_scores_gemma":[0.006321825,0.000311537,0.001197483,0.0009822784,0.001357287,0.001727786,0.001716468,0.00214647,0.0005666974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002179203,"about_ca_system_score_gemma":0.001657435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06497157,"about_ca_topic_score_gemma":0.02809463,"domain_scores_codex":[0.9994363,0.0002277218,0.00002665517,0.00006746278,0.00005286717,0.0001889044],"domain_scores_gemma":[0.9959721,0.002596807,0.0004941992,0.0001499885,0.000461743,0.0003252416],"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":[0.0001737476,0.0001133921,0.0113055,0.0001077628,0.0000780812,0.001645898,0.0004715444,0.7311668,0.0004696422,0.2435288,0.005110221,0.005828466],"study_design_scores_gemma":[0.00003525209,0.00004789878,0.001379674,0.00001552165,0.00002945268,0.0002252759,0.00032108,0.963912,0.00007176353,0.03269697,0.001244569,0.00002053144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7623414,0.001948609,0.1566261,0.009768113,0.000200382,0.0001416425,0.001742873,0.0003602534,0.06687065],"genre_scores_gemma":[0.9765599,0.0004152826,0.004382875,0.0001192339,0.00004615027,0.00004847275,0.0002784919,0.00003123869,0.01811841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06497157,"threshold_uncertainty_score":0.1291867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6410304594812638,"score_gpt":0.5034322204246267,"score_spread":0.1375982390566372,"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."}}