Create <epiparameter> object(s) directly from the epiparameter library (database)

Extract <epiparameter> object(s) directly from the library of epidemiological parameters. The epiparameter library of epidemiological parameters is compiled from primary literature sources. The list output from epiparameter_db() can be subset by the data it contains, for example by: disease, pathogen, epidemiological distribution, sample size, region, etc.

If a distribution from a specific study is required, the author argument can be specified.

Multiple entries (<epiparameter> objects) can be returned, use the arguments to subset entries and use single_epiparameter = TRUE to force a single <epiparameter> to be returned.

Usage

epiparameter_db(
  disease = "all",
  pathogen = "all",
  epi_name = "all",
  author = NULL,
  subset = NULL,
  single_epiparameter = FALSE
)

Arguments

disease

A character string specifying the disease.

pathogen

A character string specifying the pathogen.

epi_name

A character string specifying the epidemiological parameter. See details for full list of epidemiological distributions.

author

A character string specifying the author of the study reporting the distribution. Only the first author will be matched. It is recommended to use the family name as first names may or may not be initialised.

subset

Either NULL or a valid R expressions that evaluates to logicals to subset the list of <epiparameter>, or a function that can be applied over a list of <epiparameter> objects.

Subsetting (using subset) can be combined with the subsetting done with the disease and epi_name arguments (and author if specified). If left as NULL (default) no subsetting is carried out.

The subset argument is similar to subsetting a <data.frame>, but the difference is that fixed comparisons and not vectorised comparisons are needed. For example sample_size > 10 is a valid subset expression, but sample_size == max(sample_size), which would be a valid subset expression for a <data.frame> does not work. The vectorised expression will often not error, but will likely return unexpected results. For the sample_size == max(sample_size) example it will always return TRUE (except for NAs) as it is a single numeric so will be equal to it's max value.

The expression should be specified without using the data object name (e.g. df$var) and instead just var should be supplied. In other words, this argument uses non-standard evaluation, just as the subset argument in subset(), and is similar to <data-masking> used by the dplyr package.

single_epiparameter

A boolean logical determining whether a single <epiparameter> or multiple entries from the library can be returned if matched by the other arguments (disease, epi_name, author). This argument is used to prevent multiple sets of parameters being returned when only one is wanted.

Note: If multiple entries match the arguments supplied and single_epiparameter = TRUE then the <epiparameter> that is parameterised (and accounts for truncation if available) and has the largest sample size will be returned (see is_parameterised()). If multiple entries are equal after this sorting the first entry will be returned.

Value

An <epiparameter> object or list of <epiparameter> objects.

Details

disease, epi_name and author are given as individual arguments as these are the most common variables to subset the parameter library by. The subset argument facilitates all other subsetting of rows to select the <epiparameter> object(s) desired. To subset based on multiple variables separate each expression with &.

List of epidemiological parameters:

"all" (default, returns all entries in library)
"incubation period"
"onset to hospitalisation"
"onset to death"
"serial interval"
"generation time"
"offspring distribution"
"hospitalisation to death"
"hospitalisation to discharge"
"notification to death"
"notification to discharge"
"onset to discharge"
"onset to ventilation"

Examples

epiparameter_db(disease = "influenza", epi_name = "serial_interval")
#> Returning 1 results that match the criteria (1 are parameterised). 
#> Use subset to filter by entry variables or single_epiparameter to return a single entry. 
#> To retrieve the citation for each use the 'get_citation' function
#> Disease: Influenza
#> Pathogen: Influenza-A-H1N1Pdm
#> Epi Parameter: serial interval
#> Study: Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S,
#> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White
#> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early
#> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.”
#> _PLoS Currents_. doi:10.1371/currents.RRN1130
#> <https://doi.org/10.1371/currents.RRN1130>.
#> Distribution: gamma (days)
#> Parameters:
#>   shape: 2.622
#>   scale: 0.957

# example using custom subsetting
eparam <- epiparameter_db(
  disease = "SARS",
  epi_name = "offspring_distribution",
  subset = sample_size > 40
)
#> Returning 1 results that match the criteria (1 are parameterised). 
#> Use subset to filter by entry variables or single_epiparameter to return a single entry. 
#> To retrieve the citation for each use the 'get_citation' function

# example using functional subsetting
eparam <- epiparameter_db(
  disease = "COVID-19",
  epi_name = "incubation_period",
  subset = is_parameterised
)
#> Returning 11 results that match the criteria (11 are parameterised). 
#> Use subset to filter by entry variables or single_epiparameter to return a single entry. 
#> To retrieve the citation for each use the 'get_citation' function

# example forcing a single <epiparameter> to be returned
eparam <- epiparameter_db(
  disease = "SARS",
  epi_name = "offspring_distribution",
  single_epiparameter = TRUE
)
#> Using Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and
#> the effect of individual variation on disease emergence.” _Nature_.
#> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>.. 
#> To retrieve the citation use the 'get_citation' function