Skip to content

Latest commit

 

History

History
286 lines (242 loc) · 11.6 KB

README.md

File metadata and controls

286 lines (242 loc) · 11.6 KB
Raw

epiparameter

License: MIT R-CMD-check Codecov test coverage Lifecycle: experimental DOI

{epiparameter} is an R package that contains a library of epidemiological parameters for infectious diseases and a set of 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.

Installation

The easiest way to install the development version of {epiparameter} is to use 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"))

Quick start

library(epiparameter)

To load the library of epidemiological parameters into R:

epidists <- epidist_db()
#> Returning 122 results that match the criteria (99 are parameterised). 
#> Use subset to filter by entry variables or single_epidist to return a single entry. 
#> To retrieve the citation for each use the 'get_citation' function
epidists
#> # List of 122 <epidist> 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 distributions: 12
#> ❯ 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 Distribution: 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
#> Parameters:
#>   meanlog: 1.247
#>   sdlog: 0.975
#> 
#> [[2]]
#> Disease: Human Coronavirus
#> Pathogen: Human_Cov
#> Epi Distribution: 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)70069-7
#> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>.
#> Distribution: lnorm
#> Parameters:
#>   meanlog: 0.742
#>   sdlog: 0.918
#> 
#> [[3]]
#> Disease: SARS
#> Pathogen: SARS-Cov-1
#> Epi Distribution: 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)70069-8
#> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>.
#> Distribution: lnorm
#> Parameters:
#>   meanlog: 0.660
#>   sdlog: 1.205
#> 
#> # ℹ 119 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 a list of database entries. Each entry of the library is an <epidist> 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_epidist = TRUE as we only want a single database entry returned, and by default (single_epidist = FALSE) it will return all database entries that match the disease (disease) and epidemiological distribution (epi_dist).

influenza_incubation <- epidist_db(
  disease = "influenza",
  epi_dist = "incubation period",
  single_epidist = 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 Distribution: 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
#> Parameters:
#>   shape: 2.101
#>   scale: 3.839

To quickly view the list of epidemiological distributions returned by epidist_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 distribution (epi_dist).

parameter_tbl(epidists)
#> # Parameter table:
#> # A data frame:    122 × 7
#>    disease  pathogen epi_distribution prob_distribution author  year sample_size
#>    <chr>    <chr>    <chr>            <chr>             <chr>  <dbl>       <dbl>
#>  1 Adenovi… Adenovi… incubation peri… lnorm             Lessl…  2009          14
#>  2 Human C… Human_C… incubation peri… lnorm             Lessl…  2009          13
#>  3 SARS     SARS-Co… incubation peri… lnorm             Lessl…  2009         157
#>  4 Influen… Influen… incubation peri… lnorm             Lessl…  2009         151
#>  5 Influen… Influen… incubation peri… lnorm             Lessl…  2009          90
#>  6 Influen… Influen… incubation peri… lnorm             Lessl…  2009          78
#>  7 Measles  Measles… incubation peri… lnorm             Lessl…  2009          55
#>  8 Parainf… Parainf… incubation peri… lnorm             Lessl…  2009          11
#>  9 RSV      RSV      incubation peri… lnorm             Lessl…  2009          24
#> 10 Rhinovi… Rhinovi… incubation peri… lnorm             Lessl…  2009          28
#> # ℹ 112 more rows
parameter_tbl(
  epidists,
  epi_dist = "onset to hospitalisation"
)
#> # Parameter table:
#> # A data frame:    5 × 7
#>   disease  pathogen  epi_distribution prob_distribution author  year sample_size
#>   <chr>    <chr>     <chr>            <chr>             <chr>  <dbl>       <dbl>
#> 1 MERS     MERS-Cov  onset to hospit… <NA>              Assir…  2013          23
#> 2 COVID-19 SARS-CoV… onset to hospit… gamma             Linto…  2020         155
#> 3 COVID-19 SARS-CoV… onset to hospit… gamma             Linto…  2020          34
#> 4 COVID-19 SARS-CoV… onset to hospit… lnorm             Linto…  2020         155
#> 5 COVID-19 SARS-CoV… onset to hospit… lnorm             Linto…  2020          34

The <epidist> object can be plotted.

plot(influenza_incubation)

The CDF can also be plotted by setting cumulative = TRUE.

plot(influenza_incubation, cumulative = TRUE)

Parameter conversion and extraction

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:

  • gamma
  • lognormal
  • Weibull
  • negative binomial
  • geometric

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:

  • gamma
  • lognormal
  • Weibull
  • normal

Contributing to library of epidemiological parameters

If you would like to contribute to the different epidemiological parameters stored in the {epiparameter} package, you can add data to a public google sheet. This spreadsheet contains two example entries as a guide to what fields can accept. We are monitoring this sheet for new entries that will subsequently be included in the package.

Alternatively, parameters can be added to the JSON file holding the data base directly via a Pull Request.

You can find explanation of accepted entries for each column in the data dictionary.

Help

To report a bug please open an issue

Contribute

Contributions to {epiparameter} are welcomed. package contributing guide.

Code of Conduct

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.

Citing this package

citation("epiparameter")
#> To cite package 'epiparameter' in publications use:
#> 
#>   Lambert J, Kucharski A, Tamayo C (2024). _epiparameter: Library of
#>   Epidemiological Parameters with Helper Functions and Classes_.
#>   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: Library of Epidemiological Parameters with Helper Functions and Classes},
#>     author = {Joshua W. Lambert and Adam Kucharski and Carmen Tamayo},
#>     year = {2024},
#>     doi = {10.5281/zenodo.11110881},
#>     url = {https://epiverse-trace.github.io/epiparameter},
#>   }