{"id":"W3102573622","doi":"10.2196/21168","title":"Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Herd immunity; Transmission (telecommunications); Pandemic; Case fatality rate; Immunity; Population; Virology; Infectivity; Disease; Immunology; Serology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Coronavirus; Biology; Virus; Coronavirus disease 2019 (COVID-19); Infectious disease (medical specialty); Environmental health; Immune system; Antibody; Computer science; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002980243,0.0008556149,0.0009385057,0.0008447631,0.0003008047,0.00095236,0.001869857,0.001443434,0.002517032],"category_scores_gemma":[0.006943013,0.000519195,0.001664328,0.0005125754,0.001040791,0.001002277,0.0008324141,0.001268357,0.0003777884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091497,"about_ca_system_score_gemma":0.0006984264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01606006,"about_ca_topic_score_gemma":0.007328077,"domain_scores_codex":[0.9992771,0.0004054632,0.00002929025,0.0001408528,0.00005007,0.00009724881],"domain_scores_gemma":[0.9949084,0.003846013,0.00065105,0.0001577189,0.000281145,0.0001557892],"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":[0.00005254902,0.00002584501,0.003170105,0.00001836899,0.00003019306,0.00007097641,0.00003163065,0.9915574,0.0001392589,0.003788937,0.000198982,0.0009158012],"study_design_scores_gemma":[0.000006382486,0.00002021996,0.0003406857,0.00000319831,0.000007648429,0.00001389993,0.00001461083,0.9985396,0.00002477921,0.0009662,0.00005930804,0.000003519111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7633101,0.0007632812,0.2254415,0.001525278,0.0001041621,0.0001187005,0.001247459,0.0003004435,0.007189063],"genre_scores_gemma":[0.9845039,0.000310969,0.01004712,0.000115624,0.00003992249,0.00009053944,0.0004836065,0.00003340109,0.004374959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01606006,"threshold_uncertainty_score":0.03193313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5193111230676334,"score_gpt":0.4830931738480934,"score_spread":0.03621794921954002,"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."}}