{epiparameter} is an R package that contains a library of
epidemiological parameters for infectious diseases as well as classes
and helper functions to work with the data. It also includes functions
to extract and convert parameters from reported summary statistics.
{epiparameter} is developed at the Centre for the Mathematical
Modelling of Infectious
Diseases
at the London School of Hygiene and Tropical
Medicine as part of
Epiverse-TRACE.
The package can be installed from CRAN using
install.packages("epiparameter")
The development version of {epiparameter} can be installed from
GitHub using the
{pak} package:
# check whether {pak} is installed
if(!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/epiparameter")
Alternatively, install pre-compiled binaries from the Epiverse TRACE R-universe
install.packages("epiparameter", repos = c("https://epiverse-trace.r-universe.dev", "https://cloud.r-project.org"))
library(epiparameter)
To load the library of epidemiological parameters into R:
epiparameters <- epiparameter_db()
#> Returning 133 results that match the criteria (108 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
epiparameters
#> # List of 133 <epiparameter> objects
#> Number of diseases: 23
#> ❯ Adenovirus ❯ Chikungunya ❯ COVID-19 ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ Marburg Virus Disease ❯ Measles ❯ MERS ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ Rhinovirus ❯ Rift Valley Fever ❯ RSV ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease
#> Number of epi parameters: 13
#> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval
#> [[1]]
#> Disease: Adenovirus
#> Pathogen: Adenovirus
#> Epi Parameter: incubation period
#> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009).
#> "Incubation periods of acute respiratory viral infections: a systematic
#> review." _The Lancet Infectious Diseases_.
#> doi:10.1016/S1473-3099(09)70069-6
#> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>.
#> Distribution: lnorm (days)
#> Parameters:
#> meanlog: 1.723
#> sdlog: 0.231
#>
#> [[2]]
#> Disease: Human Coronavirus
#> Pathogen: Human Coronavirus
#> Epi Parameter: incubation period
#> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009).
#> "Incubation periods of acute respiratory viral infections: a systematic
#> review." _The Lancet Infectious Diseases_.
#> doi:10.1016/S1473-3099(09)70069-6
#> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>.
#> Distribution: lnorm (days)
#> Parameters:
#> meanlog: 1.163
#> sdlog: 0.140
#>
#> [[3]]
#> Disease: SARS
#> Pathogen: SARS-CoV-1
#> Epi Parameter: incubation period
#> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009).
#> "Incubation periods of acute respiratory viral infections: a systematic
#> review." _The Lancet Infectious Diseases_.
#> doi:10.1016/S1473-3099(09)70069-6
#> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>.
#> Distribution: lnorm (days)
#> Parameters:
#> meanlog: 1.386
#> sdlog: 0.593
#>
#> # ℹ 130 more elements
#> # ℹ Use `print(n = ...)` to see more elements.
#> # ℹ Use `parameter_tbl()` to see a summary table of the parameters.
#> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html
This results in a list of database entries. Each entry of the library is
an <epiparameter> object.
Alternatively, the library of epiparameters can be viewed as a vignette
locally (vignette("database", package = "epiparameter")) or on the
{epiparameter}
website.
The results can be filtered by disease and epidemiological distribution.
Here we set single_epiparameter = TRUE as we only want a single
database entry returned, and by default (single_epiparameter = FALSE)
it will return all database entries that match the disease (disease)
and epidemiological parameter (epi_name).
influenza_incubation <- epiparameter_db(
disease = "influenza",
epi_name = "incubation period",
single_epiparameter = TRUE
)
#> Using Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau
#> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). "Estimating the
#> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9)
#> Virus Infections." _American Journal of Epidemiology_.
#> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>..
#> To retrieve the citation use the 'get_citation' function
influenza_incubation
#> Disease: Influenza
#> Pathogen: Influenza-A-H7N9
#> Epi Parameter: incubation period
#> Study: Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau
#> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). "Estimating the
#> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9)
#> Virus Infections." _American Journal of Epidemiology_.
#> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>.
#> Distribution: weibull (days)
#> Parameters:
#> shape: 2.101
#> scale: 3.839
To quickly view the list of epidemiological distributions returned by
epiparameter_db() in a table, the parameter_tbl() gives a summary of
the data, and offers the ability to subset you data by disease,
pathogen and epidemiological parameter (epi_name).
parameter_tbl(epiparameters)
#> # Parameter table:
#> # A data frame: 133 × 7
#> disease pathogen epi_name prob_distribution author year sample_size
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Adenovirus Adenovi… incubat… lnorm Lessl… 2009 14
#> 2 Human Coronavir… Human C… incubat… lnorm Lessl… 2009 13
#> 3 SARS SARS-Co… incubat… lnorm Lessl… 2009 157
#> 4 Influenza Influen… incubat… lnorm Lessl… 2009 151
#> 5 Influenza Influen… incubat… lnorm Lessl… 2009 90
#> 6 Influenza Influen… incubat… lnorm Lessl… 2009 78
#> 7 Measles Measles… incubat… lnorm Lessl… 2009 55
#> 8 Parainfluenza Parainf… incubat… lnorm Lessl… 2009 11
#> 9 RSV RSV incubat… lnorm Lessl… 2009 24
#> 10 Rhinovirus Rhinovi… incubat… lnorm Lessl… 2009 28
#> # ℹ 123 more rows
parameter_tbl(
epiparameters,
epi_name = "onset to hospitalisation"
)
#> # Parameter table:
#> # A data frame: 5 × 7
#> disease pathogen epi_name prob_distribution author year sample_size
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 MERS MERS-CoV onset to hospi… <NA> Assir… 2013 23
#> 2 COVID-19 SARS-CoV-2 onset to hospi… gamma Linto… 2020 155
#> 3 COVID-19 SARS-CoV-2 onset to hospi… gamma Linto… 2020 34
#> 4 COVID-19 SARS-CoV-2 onset to hospi… lnorm Linto… 2020 155
#> 5 COVID-19 SARS-CoV-2 onset to hospi… lnorm Linto… 2020 34
The <epiparameter> object can be plotted.
plot(influenza_incubation)
The CDF can also be plotted by setting cumulative = TRUE.
plot(influenza_incubation, cumulative = TRUE)
The parameters of a distribution can be converted to and from mean and
standard deviation. In {epiparameter} we implement this for a variety
of distributions:
The parameters of a probability distribution can also be extracted from other summary statistics, for example, percentiles of the distribution, or the median and range of the data. This can be done for:
The library of epidemiological parameters that can be loaded by
{epiparameter} using the epiparameter_db() function is stored in the
{epiparameterDB} R
package. To
contribute an entry, add it to the JSON file holding the
database
via a pull request to that repository. The Epiverse-TRACE contributing
guide
covers the pull request process; the format of an entry is described in
the Epidemiological parameter data
sources
vignette.
The library is a development and experimentation set rather than a
comprehensive repository. It stores parameters needed by
{epiparameter}, another Epiverse-TRACE package or a tutorial that are
not available elsewhere. Parameters intended for long-term curation are
better contributed to
grEPI or
{epireview} directly.
You can find a description of the epidemiological parameter data structure and contents in the data dictionary. This documents the valid format and data types to ensure consistency and accuracy. All entries in the parameter library are automatically validated against the data dictionary using an GitHub action workflow.
To report a bug please open an issue
Contributions to {epiparameter} are welcomed. package contributing
guide.
Please note that the {epiparameter} project is released with a
Contributor Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
citation("epiparameter")
#> To cite package 'epiparameter' in publications use:
#>
#> Lambert J, Kucharski A, Tamayo Cuartero C (2026). _epiparameter:
#> Classes and Helper Functions for Working with Epidemiological
#> Parameters_. doi:10.5281/zenodo.11110881
#> <https://doi.org/10.5281/zenodo.11110881>.
#> <https://epiverse-trace.github.io/epiparameter/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {epiparameter: Classes and Helper Functions for Working with Epidemiological Parameters},
#> author = {Joshua W. Lambert and Adam Kucharski and Carmen {Tamayo Cuartero}},
#> year = {2026},
#> doi = {10.5281/zenodo.11110881},
#> url = {https://epiverse-trace.github.io/epiparameter/},
#> }
{epiparameter} provides classes and helper functions for working with
epidemiological parameters. It is not the only source of
already-estimated parameters:
{epiparameterDB}
holds the library of epidemiological parameters loaded by
epiparameter_db().
{epireview} provides
parameters for a range of pathogens extracted from the literature by
the Pathogen Epidemiology Review Group (PERG) in systematic reviews.
Its parameter tables can be converted with as_epiparameter(), as
described in the Using {epireview} with
{epiparameter}
article.
grEPI, the Global Epidemiological Parameters database hosted on the
WHO Collaboratory, can be queried
directly with epiparameter_db(db = "grEPI").
{epiparameter} also does not estimate parameters from data. The
following packages do, and are intended to be used alongside
{epiparameter}:
{primarycensored} fits
delay distributions while accounting for primary event censoring,
secondary event interval censoring and right truncation. It extends
{fitdistrplus},
and is a good starting point when fitting delays to individual-level
data.
{epidist} estimates
epidemiological delay distributions in a Bayesian framework, also
accounting for censoring and truncation.
{EpiNow2} provides
estimate_delay() for fitting delay distributions, and
estimate_truncation() for estimating and adjusting for right
truncation.
{fitdistrplus} is
a general-purpose distribution fitting package. It does not handle
discretisation, censoring or truncation itself, so for epidemiological
delays it is best used through {primarycensored}.
{epitrix} provides
fit_disc_gamma() for fitting a discretised gamma distribution.
Estimates obtained from completed delays alone can be subject to epidemic phase bias and right truncation, so we recommend the packages above that account for these.