Climate-driven statistical models as effective predictors of local dengue incidence in costa rica: a generalized additive model and random forest approach

DOI10.15517/RMTA.V27I1.39931OpenAlexW2995891038MaRDI QIDQ5041958

Luis A. Barboza, Antonio Loría, Paola Vásquez, Fabio Sanchez

Publication date: 18 October 2022

Published in: Revista de Matemática: Teoría y Aplicaciones (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.13095

zbMATH Keywords

random forestsgeneralized additive modelsdenguemosquito-borne diseasesCosta Ricaclimate variables

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Epidemiology (92D30) General nonlinear regression (62J02)

Uses Software

• ElemStatLearn
• gamair

Cites Work

• Unnamed Item
• Parameter estimates of the 2016--2017 Zika outbreak in Costa Rica: an approximate Bayesian computation (ABC) approach
• Generalized Additive Models: Some Applications
• Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
• Random forests

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