Title: Statistical and Viz Tools for Vector-Borne Diseases in Colombia
Version: 1.0.1
Description: Provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) https://iris.paho.org/handle/10665.2/8562).
License: MIT + file LICENSE
URL: https://epiverse-trace.github.io/epiCo/, https://github.com/epiverse-trace/epiCo
BugReports: https://github.com/epiverse-trace/epiCo/issues
Depends: R (≥ 4.0.0)
Imports: dplyr, ggplot2, ggraph, grDevices, igraph, incidence, leaflet, lubridate, magrittr, rlang, scales, spdep, stats, treemapify, utils
Suggests: checkmate, covr, knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/Needs/website: epiverse-trace/epiversetheme
Config/testthat/edition: 3
Config/potools/style: explicit
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Language: en-US
NeedsCompilation: no
Packaged: 2025-01-08 22:03:21 UTC; juandanielumanacaro
Author: Juan D. Umaña ORCID iD [aut, cre, cph], Juan Montenegro-Torres ORCID iD [aut], Julian Otero ORCID iD [aut], Hugo Gruson ORCID iD [ctb]
Maintainer: Juan D. Umaña <jd.umana10@uniandes.edu.co>
Repository: CRAN
Date/Publication: 2025-01-15 10:10:01 UTC

epiCo: Statistical and Viz Tools for Vector-Borne Diseases in Colombia

Description

Provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) https://iris.paho.org/handle/10665.2/8562).

Author(s)

Maintainer: Juan D. Umaña jd.umana10@uniandes.edu.co (ORCID) [copyright holder]

Authors:

Other contributors:

See Also

Useful links:

Returns the specific rates associated with being infected given age and sex

Description

Function that returns the specific rates of being infected given age and sex

Usage

age_risk(age, population_pyramid, sex = NULL, plot = FALSE)

Arguments

age

A vector with the ages of cases in years from 0 to 100 years

population_pyramid

A dataframe with the count of individuals with the columns age, population and sex

sex

A vector with the sex of cases 'F' and 'M'. The default value is NULL

plot

A boolean for displaying a plot. The default value is FALSE

Value

A dataframe with the proportion or total count of individuals

Examples

pop_pyramid <- population_pyramid("15001", 2015,
  sex = TRUE, total = TRUE,
  plot = FALSE
)
ages <- round(runif(150, 0, 100))
sex <- c(rep("M", 70), rep("F", 80))
age_risk(
  age = ages, sex = sex, population_pyramid = pop_pyramid,
  plot = TRUE
)

Provides the sociological description of ethnicities in Colombia

Description

Function that returns the description of the consulted ethnicities

Usage

describe_ethnicity(ethnic_codes)

Arguments

ethnic_codes

A numeric vector with the codes of ethnicities to consult

Value

A printed message with ethnicities descriptions

Examples

describe_ethnicity(round(runif(n = 150, min = 1, max = 4)))

Get ISCO-88 occupation labels from codes

Description

Function that translates a vector of ISCO-88 occupation codes into a vector of labels

Usage

describe_occupation(isco_codes, sex = NULL, plot = NULL)

Arguments

isco_codes

A numeric vector of ISCO-88 occupation codes (major, submajor, minor, or unit)

sex

A vector with the respective sex for isco_codes vector. The default value is NULL

plot

A type of plot between treemap and circular packing. The default value is NULL

Value

A string vector of ISCO-88 labels

Examples

demog_data <- data.frame(
  occupation_label =
    c(6111, 3221, 5113, 5133, 6111, 23, 25),
  sex = c("F", "M", "F", "F", "M", "M", "F")
)
describe_occupation(
  isco_codes = demog_data$occupation_label,
  sex = demog_data$sex, plot = "treemap"
)

divipola_table

Description

Political and administrative distribution of Colombia's municipalities

Usage

data(divipola_table)

Format

An object of class data.frame with 1121 rows and 8 columns.

Details

DIVIPOLA table

Create and return the endemic channel of a disease from an incidence object

Description

Function that builds the endemic channel of a disease time series based on the selected method and windows of observation

Usage

endemic_channel(
  incidence_historic,
  observations = NULL,
  method = c("geometric", "median", "mean", "unusual_behavior"),
  geometric_method = "shifted",
  outlier_years = NULL,
  outliers_handling = c("ignored", "included", "replaced_by_median", "replaced_by_mean",
    "replaced_by_geometric_mean"),
  ci = 0.95,
  plot = FALSE
)

Arguments

incidence_historic

An incidence object with the historic weekly observations

observations

A numeric vector with the current observations

method

A string with the mean calculation method of preference (median, mean, or geometric) or to use the unusual behavior method (Poisson Distribution Test for Hypoendemic settings)

geometric_method

A string with the selected method for geometric mean calculation; see: geometric_mean

outlier_years

A numeric vector with the outlier years

outliers_handling

A string with the handling decision regarding outlier years, see: outliers_handling function

ci

= 0.95 A numeric value to specify the confidence interval to use with the geometric method

plot

A boolean for displaying a plot

Value

A dataframe with the observation, historical mean, and confidence intervals (or risk areas)

Examples

data_event <- epiCo::epi_data
data_ibague <- data_event[data_event$cod_mun_o == 73001, ]
incidence_historic <- incidence::incidence(data_ibague$fec_not,
  interval = "1 epiweek"
)
endemic_channel(incidence_historic,
  method = "geometric", plot = TRUE
)

Modifies the historic incidence to handle with the observations of epidemic years

Description

Function that modifies an historic incidence by including, ignoring or replacing the observations of epidemic years

Usage

endemic_outliers(
  historic,
  outlier_years,
  outliers_handling,
  geometric_method = "shifted"
)

Arguments

historic

Historic incidence counts

outlier_years

A numeric vector with the outlier years

outliers_handling

A string with the handling decision regarding outlier years

  • ignored = data from outlier years will not take into account

  • included = data from outlier years will take into account

  • replaced_by_median = data from outlier years will be replaced with the median and take into account

  • replaced_by_mean = data from outlier years will be replaced with the mean and take into account

  • replaced_by_geometric_mean = data from outlier years will be replaced with the geometric mean and take into account

geometric_method

A string with the selected method for geometric mean calculation; see: geometric_mean

Value

A modified historic incidence

Creates the endemic channel plot

Description

Function that creates the endemic channel plot

Usage

endemic_plot(channel_data, method, outlier_years, outliers_handling)

Arguments

channel_data

Data frame with the central tendency, upper limit, lower limit, and observations to plot

method

A string with the method used in the endemic channel calculation

outlier_years

A numeric vector with the outlier years

outliers_handling

A string with the handling decision regarding outlier years

Value

The ggplot object with the endemic channel plot

Get the epidemiological calendar of a consulted year.

Description

Function that returns the starting date of the epidemiological weeks in a year of interest.

Usage

epi_calendar(year, jan_days = 4)

Arguments

year

A numeric value for the year of interest.

jan_days

Number of January days that the first epidemiological week must contains.

Value

A character array with the starting dates of the epidemiological weeks of the given year.

Examples

epi_calendar(2016)

epi_data

Description

Epidemiological data for the Tolima department for the years 2012 to 2022

Usage

data(epi_data)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 66747 rows and 16 columns.

Details

Epidemiological data

Returns the geometric mean of a vector of real numbers.

Description

Function that returns the geometric mean of a vector of real numbers according to the selected method.

Usage

geometric_mean(
  x,
  method = c("positive", "shifted", "optimized", "weighted"),
  shift = 1,
  epsilon = 0.001
)

Arguments

x

A numeric vector of real values

method

Description of methods:

  • positive = only positive values within x are used in the calculation.

  • shifted = positive and zero values within x are used by adding a shift value before the calculation and subtracting it to the final result.

  • optimized = optimized shifted method. See: De La Cruz, R., & Kreft, J. U. (2018). Geometric mean extension for data sets with zeros. arXiv preprint arXiv:1806.06403.

  • weighted = a probability weighted calculation of gm for negative, positive, and zero values. See: Habib, E. A. (2012). Geometric mean for negative and zero values. International Journal of Research and Reviews in Applied Sciences, 11(3), 419-432.

shift

= 1 (default) a positive value to use in the shifted method

epsilon

= 1e-5 (default) the minimum positive value to consider in the optimized method.

Value

The geometric mean of the x vector, and the epsilon value if optimized method is used.

Examples

x <- c(4, 5, 3, 7, 8)
geometric_mean(x, method = "optimized")

Returns the geometric standard deviation of a vector of real numbers.

Description

Function that returns the geometric standard deviation of a vector of real numbers according to the selected method.

Usage

geometric_sd(
  x,
  method = c("positive", "shifted", "optimized", "weighted"),
  shift = 1,
  delta = 0.001
)

Arguments

x

A numeric vector of real values

method

Description of methods:

  • positive = only positive values within x are used in the calculation.

  • shifted = positive and zero values within x are used by adding a shift value before the calculation and subtracting it to the final result.

  • optimized = optimized shifted method. See: De La Cruz, R., & Kreft, J. U. (2018). Geometric mean extension for data sets with zeros. arXiv preprint arXiv:1806.06403.

  • weighted = a probability weighted calculation of gm for negative, positive, and zero values. See: Habib, E. A. (2012). Geometric mean for negative and zero values. International Journal of Research and Reviews in Applied Sciences, 11(3), 419-432.

shift

a positive value to use in the shifted method

delta

an positive value (shift) used in the optimized method.

Value

The geometric mean of the x vector, and the epsilon value if optimized method is used.

Examples

x <- c(4, 5, 3, 7, 8)
geometric_sd(x, method = "optimized")

Auxiliary function to calculate the proportion by age according to the total population and sex

Description

Auxiliary function to calculate the proportion by age according to the total population and sex

Usage

get_age_risk_sex(age, sex_vector, pyramid, sex)

Arguments

age

A vector with the ages of cases in years from 0 to 100 years

sex_vector

A vector with the sex of cases 'F' and 'M'

pyramid

A dataframe with the count of individuals

sex

A string specifying the sex being calculated

Value

A dataframe with the proportion by age according to the total population and sex

Auxiliary function to obtain the information of the occupations

Description

Auxiliary function to obtain the information of the occupations

Usage

get_occupation_data(
  valid_codes,
  isco_codes,
  isco88_table,
  name_occupation,
  sex = NULL
)

Arguments

valid_codes

A numeric vector with the valid codes from the ISCO-88 table

isco_codes

A numeric vector of ISCO-88 occupation codes (major, submajor, minor, or unit)

isco88_table

The ISCO-88 table columns of the information for that group of occupations

name_occupation

The category of occupations to be consulted. These can be: major, submajor, minor, or unit

sex

A vector with the respective sex for isco_codes vector. The default value is NULL

Value

A dataframe with the information of the occupations

Extends an incidence class object with incidence rates estimations.

Description

Function that estimates incidence rates from a incidence class object and population projections.

Usage

incidence_rate(incidence_object, level, scale = 1e+05)

Arguments

incidence_object

An incidence object.

level

Administration level at which incidence counts are grouped (0 = national, 1 = state/department, 2 = city/municipality).

scale

Scale to consider when calculating the incidence_rate.

Value

A modified incidence object where counts are normalized with the population.

Examples

data_event <- epiCo::epi_data
incidence_historic <- incidence::incidence(data_event$fec_not,
  groups = data_event$cod_mun_o,
  interval = "1 year"
)
incidence_object <- subset(incidence_historic,
  from = "2015-01-04",
  to = "2018-12-27"
)
inc_rate <- incidence_rate(incidence_object, level = 2, scale = 100000)
inc_rate$rates

isco88_table

Description

ISCO88 description of occupations

Usage

data(isco88_table)

Format

An object of class data.frame with 390 rows and 8 columns.

Details

ISCO88 occupation table

Calculate spatial correlation of given municipalities in an incidence_rate object.

Description

Function to calculate spatial autocorrelation via Moran's Index from a given incidence_rate object grouped by municipality.

Usage

morans_index(incidence_object, scale = 1e+05, threshold = 2, plot = TRUE)

Arguments

incidence_object

An incidence object with one observation for the different locations (groups).

scale

Scale to consider when calculating the incidence_rate.

threshold

Maximum traveling time around each municipality.

plot

if TRUE, returns a plot of influential observations in the Moran's plot.

Value

List of Moran's I clustering analysis, giving the quadrant of each observation, influential values.

Examples

data_event <- epiCo::epi_data
incidence_historic <- incidence::incidence(data_event$fec_not,
  groups = data_event$cod_mun_o,
  interval = "4 year"
)
incidence_object <- subset(incidence_historic,
  from = "2015-01-04",
  to = "2018-12-27"
)
morans_index(incidence_object, scale = 100000, threshold = 2, plot = TRUE)

Neighborhoods from real travel distances in Colombia

Description

Function to build neighborhoods from real travel distances inside Colombia by land or river transportation.

Usage

neighborhoods(query_vector, threshold = 2)

Arguments

query_vector

Codes of the municipalities to consider for the neighborhoods.

threshold

Maximum traveling time around each municipality.

Value

neighborhood object according to the introduced threshold.

Examples

query_vector <- c("05001", "05002", "05004", "05021", "05030", "05615")
neighborhoods(query_vector, 2)

Distribution plots for ISCO-88 occupation labels

Description

Function that makes a treemap plot of a vector of ISCO-88 occupation codes

Usage

occupation_plot(occupation_data, sex = FALSE, q = 0.9)

Arguments

occupation_data

A dataframe

sex

A boolean for sex data. The default value is FALSE

q

A number that represents the quantile. The default value is 0.9

Value

A plot to summarize the distribution of ISCO-88 labels

Distribution plots for ISCO-88 occupation labels

Description

Function that makes a circular packing plot of a vector of ISCO-88 occupation codes

Usage

occupation_plot_circular(occupation_data, q = 0.9)

Arguments

occupation_data

A dataframe

q

A number that represents the quantile. The default value is 0.9

Value

A plot to summarize the distribution of ISCO-88 labels

Returns the population pyramid of the consulted region

Description

Function that returns the population pyramid of the municipality or department of a specific year

Usage

population_pyramid(
  divipola_code,
  year,
  sex = TRUE,
  range = 5,
  total = TRUE,
  plot = FALSE
)

Arguments

divipola_code

A code from the divipola table representing a department or municipality. To obtain values at the national level, code '0' is used

year

A numeric input for the year of interest

sex

A boolean to consult data disaggregated by sex. The default value is TRUE

range

A numeric value from 1 to 100 for the age range to use. The default value is 5

total

A boolean for returning the total number rather than the proportion of the country's population. The default value is TRUE

plot

A boolean for displaying a plot. The default value is TRUE

Value

A dataframe with the proportion or total count of individuals

Examples

population_pyramid("15001", 2015, sex = TRUE, total = TRUE, plot = TRUE)

Returns the population pyramid plot

Description

Function that returns the population pyramid plot of the municipality or department of a specific year

Usage

population_pyramid_plot(pop_pyramid, sex = TRUE)

Arguments

pop_pyramid

A dataframe with the age counts

sex

A boolean to consult data disaggregated by sex. The default value is TRUE

Value

the population pyramid plot