Abstract
The purpose of this study has been to investigate the dynamics of dengue transmission in the 76 municipalities that make up the main island of Puerto Rico, for weeks 1 to 4 and 32 to 36 of 2014. A Bayesian spatial model was used to study the possible relationship between incidence, socioeconomic, climatic and environmental variables. Once the models are proposed, the settings are compared with indices such as WAIC (Watanabe, 2010), to determine which one represents the data best. It is also determined by the I-Moran index if there is spatial correlation in the residuals, since the existence of the index is an indicator that the adjustment is not good to some extent. Temperature and precipitation data should have been previously interpolated, since the stations that collect them are not in the population centers, to see the calculations the reader may refer to (Hernández-González, 2017).
References
Agresti, Allan. (2013). Categorical Data Analysis (3.a Ed.). New Jersey: John Wiley & Sons.
Besag, Julian, y cols. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 120.
Bivand, Roger, y cols. (2013). Computing the Jacobian in Gaussian spatial autoregressive models: An illustrated comparison of available methods. Geographical Analysis, 45, 150-179.
Bivand, Roger, y cols. (2015). Comparing Implementations of Estimation Methods for Spatial Econometrics. Journal of Statistical Software, 63, 1-36.
Bivand, Roger S., y cols. (2013). Applied spatial data analysis with R (2.a Ed.). New York: Springer.
Bureau, U. C. (2011). Datos del Censo 2010 de Puerto Rico [Web]. Descargado de http://www.jp.gobierno.pr/Portal JP/Default.aspx?tabid=120 ([Web; accedido el 02-02-2015])
CDC, Subdivisión de Dengue, y Departamento de Salud, PR. (2014). Informe Semanal de Vigilancia del Dengue [Web]. Descargado de http://www.salud.gov.pr/Estadisticas -Registros-y-Publicaciones/Pages/Dengue.aspx ([Web; accedido el 15-02-2015])
Chien, Lung-Chang., y cols. (2014). Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence. Environment International, 74, 46-56.
Gabry, J., y Goodrich, B. (2016). rstanarm: Bayesian Applied Regression Modeling via Stan [Manual de software informático]. Descargado de https://CRAN.R-project.org/ package=rstanarm (R package version 2.13.1)
Gelfand, Allan E., y cols. (2004). Hierarchical Modeling and Analysis for Spatial Data (1.a ed.). Boca Raton, Florida: Chapman & Hall/ CRC.
Hernández-González, G. (2017). Modelo Espacial Bayesiano para la Incidencia de dengue en la Isla Principal de Puerto Rico para el año 2014 (Tesis de Máster no publicada). Sistema de Estudios de Posgrado, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, Costa Rica.
Hu, Wenbiao, y cols. (2012). Spatial Patterns and Socioecological Drivers of Dengue Fever Transmission in Queensland, Australia. Environmental Health Perspectives, 120, 260-266.
Jiang, Zhangyan, y cols. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845.
Johansson, Michael A., y cols. (2009). Local and Global Effects of Climate on Dengue Transmission in Puerto Rico .PLoS Negleted Tropical Diseases, 3.
Lee, D. (2013). CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors. Journal of Statistical Software, 55, 1-24.
Leroux, Brian G., y cols. (2000). Estimation of disease rates in small areas: A new mixed model for spatial dependence. Institute for Mathematics and Its Applications, 116, 179-191.
Mena, Nelson, y cols. (2011). Factors associated with incidence of dengue in Costa Rica. Revista Panamericana de Salud Publica, 29, 234-242.
Menne., Matthew J., y cols. (2012). An Overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910.
R Core Team. (2016). R: A Language and Environment for Statistical Computing [Manual de software informático]. Vienna, Austria. Descargado de https://www.R-project.org/.
Spiegelhalter, David J, Best, Nicola G., Carlin, Bradley P., y Van-Der-Linde, Angelika. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64, 583-639.
Tuck, Sean, y cols. (2015). MODISTools: MODIS Sub setting Tools [Manual de software informático]. Descargado de http://cran.r-project.org/package=MODISTools (R package version 0.94.6)
Vehtari, Aki, y cols. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 1-20.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 3571-3594.