{"id":"W2029677042","doi":"10.6000/1927-5129.2015.11.02","title":"Using PCA, Poisson and Negative Binomial Model to Study the Climatic Factor and Dengue Fever Outbreak in Lahore","year":2015,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dengue fever; Negative binomial distribution; Poisson regression; Outbreak; Incidence (geometry); Poisson distribution; Public health; Environmental health; Population; Geography; Distributed lag; Principal component analysis; Demography; Statistics; Mathematics; 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":[0.002964518,0.0007204977,0.0007302406,0.001400968,0.0006814448,0.001416802,0.0007624156,0.0006635304,0.003311083],"category_scores_gemma":[0.00703193,0.0003633868,0.001315565,0.001218509,0.0005117449,0.000857772,0.0006715473,0.001223534,0.0003247475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007041714,"about_ca_system_score_gemma":0.001468849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03479587,"about_ca_topic_score_gemma":0.0151633,"domain_scores_codex":[0.9983424,0.0009266037,0.00007369546,0.000288079,0.0001396186,0.0002296679],"domain_scores_gemma":[0.9959785,0.003091983,0.0004054167,0.0001159149,0.0002874479,0.0001207078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00059512,0.000909935,0.694756,0.0003989611,0.001440567,0.002603492,0.001870558,0.2010888,0.001689332,0.02783216,0.008336932,0.05847804],"study_design_scores_gemma":[0.00005598061,0.0003253503,0.1031306,0.00007334369,0.0002042685,0.0003909342,0.0009294729,0.8846956,0.0002658597,0.006749026,0.003118356,0.0000612446],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9166067,0.001276985,0.07411898,0.001884822,0.0002513168,0.0002804888,0.001655448,0.0002361997,0.003688969],"genre_scores_gemma":[0.9815222,0.000849213,0.0124026,0.00008905482,0.0001185752,0.000273134,0.001255237,0.00002362181,0.003466398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03479587,"threshold_uncertainty_score":0.06918669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.518911117760351,"score_gpt":0.4653681993228794,"score_spread":0.05354291843747166,"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."}}