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

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

doi:10.15517/rmta.v27i1.39931

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Revista de Matemática Teoría y Aplicaciones

versão impressa ISSN 1409-2433

Rev. Mat vol.27 no.1 San José Jan./Jun. 2020

http://dx.doi.org/10.15517/rmta.v27i1.39931

Artículo

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

Variables climáticas como predictores de la incidencia de dengue en costa rica: un enfoque de modelo aditivo generalizado y bosques aleatorios

Paola Vásquez¹

Antonio Loría²

Fabio Sanchez³

Luis A. Barboza⁴

^¹University of Costa Rica, School of Public Health, San José, Costa Rica. paola.vasquez@ucr.ac.cr

^²University of Costa Rica, CIMPA-School of Statistics. antonio.loria@ucr.ac.cr

^³University of Costa Rica, CIMPA-School of Mathematics, San José, Costa Rica. fabio.sanchez@ucr.acr.cr

^⁴University of Costa Rica, CIMPA-School of Mathematics, San José, Costa Rica. luisalberto.barboza@ucr.ac.cr

Abstract

Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country.

Keywords: mosquito-borne diseases; dengue; climate variables; Costa Rica; generalized additive models; random forests

Resumen

En países tropicales y subtropicales alrededor del mundo, el clima ha sido un factor fundamental en moldear la distribución geográfica e incidencia de los casos de dengue. En Costa Rica, un país tropical con múltiples microclimas, el dengue ha sido endémico desde 1993, con repercusiones no solo en el ámbito de la salud, sino también en el social y económico. Utilizando el número de casos de dengue y los datos climáticos del 2007- 2017, ajustamos un modelo predictivo mediante un enfoque de Modelo Aditivo Generalizado y bosques aleatorios, el cual nos permitió predecir de forma retrospectiva el riesgo relativo de dengue en cinco cantones alrededor del país.

Palabras clave: enfermedades de trasmisión vectorial; dengue; variables climáticas; Costa Rica; modelos aditivos generalizados; bosques aleatorios.

Mathematics Subject Classification: 62J02, 62M20, 92D30.

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Acknowledgements

We thank the Research Center in Pure and Applied Mathematics and the Mathematics Department at Universidad de Costa Rica for their support during the preparation of this manuscript. The authors gratefully acknowledge institutional support for project B8747 from an UCREA grant from the Vice Rectory for Research at Universidad de Costa Rica. We would like to thank the Ministry of Health and the National Institute of Meteorology for providing the necessary dengue incidence data and climate information. We also thank Oscar Calvo-Solano, for his help in completing the climate data. This article is part of a thesis project for the masters in Public Health at the University of Costa Rica.

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Received: July 29, 2019; Revised: September 13, 2019; Accepted: October 15, 2019

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