R/dengue_MS.R

In GHRexplore: Exploratory Analysis of Temporal and Spatio-Temporal Health Data

#' Dengue cases in *Mato Grosso do Sul*
#'
#' Monthly number of notified dengue cases by
#' municipality in the *Mato Grosso do Sul* state of Brazil and a set 
#' of spatial and spatio-temporal covariates.
#' 
#' @details
#' In addition to the dengue counts, the dataset contains a set of environmental,
#' socio-economic and meteo-climatic factors. This dataset is a subset of the 
#' original containing observations over the entire Brazil. 
#' 
#' @docType data
#' @format 
#' A data frame with 2,640 rows and 27 columns:
#' \describe{
#'   \item{micro_code}{Unique ID number for each micro region (11 units).}
#'   \item{micro_name}{Name of each micro region.}
#'   \item{micro_name_ibge}{Name of each micro region in IBGE format.}
#'   \item{meso_code}{Unique ID number for each meso region (4 units).}
#'   \item{meso_name}{Name of each meso region.}
#'   \item{state_code}{Unique ID number for each state (1 unit).}
#'   \item{state_name}{Name of each state.}
#'   \item{region_code}{Unique ID number given to each Brazilian Region.
#'   All observations come from the "Southeast Region".}
#'   \item{region_name}{Name of each Brazilian Region.
#'   All observations come from the "Southeast Region".}
#'   \item{biome_code}{Biome code.}
#'   \item{biome_name}{Biome name.}
#'   \item{ecozone_code}{Ecozone code.}
#'   \item{ecozone_name}{Ecozone name.}
#'   \item{main_climate}{Most prevalent climate regime in the microregion.
#'   Based on Koppen Geiger climate regimes.}
#'   \item{month}{Calendar month index, 1 = January, 12 = December.}
#'   \item{year}{Year 2000 - 2019.}
#'   \item{time}{Time index starting at 1 for January 2000.}
#'   \item{dengue_cases}{Number of notified dengue cases registered in the
#'    notifiable diseases system in Brazil (SINAN) in the microregion of
#'    reference, at the month of first symptoms.}
#'   \item{population}{Estimated population based on projections calculated
#'     using the 2000 and 2010 censuses, as well as population counts from 2007 
#'     and 2017.}
#'   \item{pop_density}{Population density (number of people per km2).}
#'   \item{tmax}{Monthly average daily maximum temperature; gridded values
#'    (at a 0.5 deg resolution) averaged across each microregion.}
#'   \item{tmin}{Monthly average daily minimum temperature; gridded values
#'    (at a 0.5 deg resolution) averaged across each microregion.}
#'   \item{pdsi}{Self-calibrated Palmer Drought Severity Index for each
#'   microregion. It measures how wet or dry a region is relative to usual
#'    conditions. Negative values represent periods of drought,
#'    positive values represent wetter periods. Calculated by taking the mean
#'    value within each microregion.}
#'   \item{urban}{Percentage of population living in urban areas (2010 census).}
#'   \item{water_network}{Percentage of population with access to the piped
#'   water network according to the 2010 census.}
#'   \item{water_shortage}{Frequency of reported water shortages per microregion
#'    between 2000 and 2016.}
#'   \item{date}{First day of the month in date format ("%d-%m-%Y").}
#' }
#' @source <https://github.com/drrachellowe/hydromet_dengue>
#' @usage data(dengue_MS)
"dengue_MS"