Data and R code to support Lowe et al., (2021). Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study. The Lancet Planetary Health (https://doi.org/10.1016/S2542-5196(20)30292-8).
To cite this repo:
Rachel Lowe. (2021). Data and R code to accompany 'Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study' (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.4632205
This study details a methodological framework to estimate the delayed and nonlinear impact of hydrometeoroloigcal hazards on dengue risk according to different socio-economic factors. The approach builds upon previous studies to model the impact of climate and socio-economic factors in Brazil by coupling distributed lag nonlinear models (DLNM) (Gasparrini et al., 2013) with space-time Bayesian hierarchical models (Lowe et al., 2014, Lowe et al., 2018) fitted using integrated nested Laplace approximations in R (R-INLA) (Lindgren et al., 2015). The modelling approach simultaneously describes space-varying, non-linear, and delayed associations between dengue incidence rates and hydrometeorological variables. These exposure-lag-response associations can reveal how hydrometeorological hazards might impact dengue risk in the months leading up to an outbreak.
A description of each file and folder is provided below:
00_load_packages_data.R: R script to load and process data needed to run DLNMs in INLA models. There is no need to run this script. It is sourced by the following scripts.
01_visual_data.R: R script to explore and visualise dengue, hydrometeorological and socio-economic datasets.
02_model_models.R: R script to run DLNM-INLA models of increasing complexity.
03_model_output.R: R script to explore and visualise model outputs from the selected model.
04_lag_nonlinear_output.R: R script to explore and visualise exposure-lag-response associations given different socio-economic scenarios.
05_sensitivity_analysis.R: R script to test sensitivity of exposure-lag-response associations.
data: a folder containing the database, data description, and a grid file for facet_geo() plots (in .csv format), and shapefiles (.shp, .shx, .dbf, .prj).
figs: a folder to save the figures generated by the R scripts.
output: a folder to save the model outputs. This also contains a subfolder named preds, with the R script used to generate the cross-validation exercise and associated output files.
hydromet_dengue.Rproj: An RStudio project file, to avoid having to set your working directory to the hydromet_dengue folder on your computer.
Download the repository as a ZIP file using the green button Clone or download above, then open the .Rproj file in RStudio to begin.
The analysis was performed using R version 4.0.2 (2020-06-22).
For any issues with the code please contact Rachel Lowe.