{"id":"W3176391915","doi":"10.2196/24630","title":"A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, Italy: Model Development","year":2021,"lang":"en","type":"article","venue":"JMIRx Med","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Dipartimento di Matematica e Informatica, Università degli Studi di Catania; Università degli Studi di Palermo","keywords":"Social distance; Pandemic; Herd immunity; Agent-based model; Outbreak; Computer science; Scale (ratio); Epidemic model; Test (biology); Coronavirus disease 2019 (COVID-19); Event (particle physics); Vaccination; Operations research; Geography; Simulation; Artificial intelligence; Engineering; Medicine; Virology; Environmental health; Population; Ecology; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353737,0.0002357495,0.0004807643,0.00006008314,0.0003055246,0.00002686224,0.0002418844,0.0001086445,0.00003264018],"category_scores_gemma":[0.00488696,0.0001312488,0.0001547881,0.0002888415,0.00008208929,0.00004412565,0.000292753,0.0002230624,0.000002520628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009271365,"about_ca_system_score_gemma":0.0004690891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003703465,"about_ca_topic_score_gemma":0.00166878,"domain_scores_codex":[0.9980345,0.0002650388,0.0006011419,0.0003702663,0.0003500807,0.0003789257],"domain_scores_gemma":[0.99755,0.00170862,0.0001793716,0.0002962257,0.0001308575,0.0001349144],"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":[0.00271275,0.001982214,0.1420854,0.002146623,0.000684247,0.00006342907,0.1490858,0.6292477,0.04079193,0.007724065,0.01869031,0.004785451],"study_design_scores_gemma":[0.005110406,0.0001968647,0.2588479,0.0001577676,0.0001531017,0.00001851075,0.004453854,0.5676262,0.001483815,0.1605266,0.0004038022,0.001021037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9105427,0.00008198165,0.07865635,0.009911431,0.00001308787,0.0006361822,0.00001880641,0.00005426682,0.00008523351],"genre_scores_gemma":[0.9950397,0.00001714106,0.003385636,0.0009704214,0.00002112305,0.0003072394,0.00000432786,0.00002188139,0.0002325326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1528026,"threshold_uncertainty_score":0.58505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2826693243208166,"score_gpt":0.4361803315641993,"score_spread":0.1535110072433827,"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."}}