epiparameter: Create an <epiparameter> object

In epiparameter: Classes and Helper Functions for Working with Epidemiological Parameters

Create an ⁠<epiparameter>⁠ object

Description

The ⁠<epiparameter>⁠ class is used to store epidemiological parameters for a single disease. These epidemiological parameters cover a variety of aspects including delay distributions (e.g. incubation periods and serial intervals, among others) and offspring distributions.

The ⁠<epiparameter>⁠ object is the functional unit provided by {epiparameter} to plug into epidemiological pipelines. Obtaining an ⁠<epiparameter>⁠ object can be achieved in two main ways:

1. The epidemiological distribution is stored in the {epiparameter} library and can be accessed by epiparameter_db().

2. The alternative method is when you have information (e.g. disease and distribution parameter estimates) and would like to input this into an ⁠<epiparameter>⁠ object in order to work in existing analysis pipelines. This is where the epiparameter() function can be used to fill out each field for which information is known.

Usage

epiparameter(
  disease,
  pathogen = NA_character_,
  epi_name,
  prob_distribution = create_prob_distribution(prob_distribution = NA_character_),
  uncertainty = create_uncertainty(),
  summary_stats = create_summary_stats(),
  citation = create_citation(),
  metadata = create_metadata(),
  method_assess = create_method_assess(),
  notes = NULL,
  auto_calc_params = TRUE,
  ...
)

Arguments

disease	A character string with name of the infectious disease.
pathogen	A character string with the name of the causative agent of disease, or NA if not known.
epi_name	A character string with the name of the epidemiological parameter type.
prob_distribution	An S3 class containing the probability distribution or a character string if the parameters of the probability distribution are unknown but the name of the distribution is known, or NA if the distribution name and parameters are unknown. Use create_prob_distribution() to create prob_distribution.
uncertainty	A list of named vectors with the uncertainty around the probability distribution parameters. If uncertainty around the parameter estimates is unknown use create_uncertainty() (which is the argument default) to create a list with the correct names with missing values.
summary_stats	A list of summary statistics, use create_summary_stats() to create list. This list can include summary statistics about the inferred distribution such as it's mean and standard deviation, quantiles of the distribution, or information about the data used to fit the distribution such as lower and upper range. The summary statistics can also include uncertainty around metrics such as confidence interval around mean and standard deviation.
citation	A ⁠<bibentry>⁠ with the citation of the source of the data or the paper that inferred the distribution parameters, use create_citation() to create citation.
metadata	A list of metadata, this can include: units, sample size, the transmission mode of the disease (e.g. is it vector-borne or directly transmitted), etc. It is assumed that the disease is not vector-borne and that the distribution is intrinsic (e.g. not an extrinsic delay distribution such as extrinsic incubation period) unless transmission_mode = "vector_borne" is contained in the metadata. Use create_metadata() to create metadata.
method_assess	A list of methodological aspects used when fitting the distribution, use create_method_assess() to create method assessment.
notes	A character string with any additional information about the data, inference method or disease.
auto_calc_params	A boolean logical determining whether to try and calculate the probability distribution parameters from summary statistics if distribution parameters are not provided. Default is TRUE. In the case when sufficient summary statistics are provided and the parameter(s) of the distribution are not, the .calc_dist_params() function is called to calculate the parameters and add them to the epiparameter object created.
...	dots Extra arguments to be passed to internal functions. This is most commonly used to pass arguments to distcrete::distcrete() that construct the discretised distribution S3 object. To see which arguments can be adjusted for discretised distributions see distcrete::distcrete().

Details

Accepted ⁠<epiparameter>⁠ distribution parameterisations are:

• Gamma must be either 'shape' and 'scale' or 'shape' and 'rate'

• Weibull must be 'shape' and 'scale'

• Lognormal must be 'meanlog' and 'sdlog' or 'mu' and 'sigma'

• Negative Binomial must be either 'mean' and 'dispersion' or 'n' and 'p'

• Geometric must be either 'mean' or 'prob'

• Poisson must be 'mean'

Value

An ⁠<epiparameter>⁠ object.

Examples

# minimal input required for `epiparameter`
ebola_incubation <- epiparameter(
  disease = "ebola",
  epi_name = "incubation_period",
  prob_distribution = create_prob_distribution(
    prob_distribution = "gamma",
    prob_distribution_params = c(shape = 1, scale = 1)
  )
)

# minimal input required for discrete `epiparameter`
ebola_incubation <- epiparameter(
  disease = "ebola",
  epi_name = "incubation_period",
  prob_distribution = create_prob_distribution(
    prob_distribution = "gamma",
    prob_distribution_params = c(shape = 1, scale = 1),
    discretise = TRUE
  )
)

# example with more fields filled in
ebola_incubation <- epiparameter(
  disease = "ebola",
  pathogen = "ebola_virus",
  epi_name = "incubation",
  prob_distribution = create_prob_distribution(
    prob_distribution = "gamma",
    prob_distribution_params = c(shape = 1, scale = 1),
    discretise = FALSE,
    truncation = NA
  ),
  uncertainty = list(
    shape = create_uncertainty(),
    scale = create_uncertainty()
  ),
  summary_stats = create_summary_stats(mean = 2, sd = 1),
  citation = create_citation(
    author = person(given = "John", family = "Smith"),
    year = 2002,
    title = "COVID-19 incubation period",
    journal = "Epi Journal",
    doi = "10.19832/j.1366-9516.2012.09147.x"
  ),
  metadata = create_metadata(
    units = "days",
    sample_size = 10,
    region = "UK",
    transmission_mode = "natural_human_to_human",
    inference_method = "MLE"
  ),
  method_assess = create_method_assess(
    censored = TRUE
  ),
  notes = "No notes"
)

epiparameter documentation built on April 3, 2025, 5:50 p.m.