bpmodels: Methods for simulating and analysing the size and length of transmission chains from branching process models

Note

{bpmodels} is now retired and will no longer be maintained. We recommend using {epichains} instead. If you need help converting your code to use {epichains}, please open a discussion on epichains.

bpmodels is an R package to simulate and analyse the size and length of branching processes with a given offspring distribution. These models are often used in infectious disease epidemiology, where the chains represent chains of transmission, and the offspring distribution represents the distribution of secondary infections caused by an infected individual.

Installation

The latest development version of the bpmodels package can be installed via

# check whether {pak} is installed
if (!require("pak")) install.packages("pak")
pak::pkg_install("epiforecasts/bpmodels")

To load the package, use

library("bpmodels")
#> Note: bpmodels is now retired and replaced by epichains. All features from bpmodels are available in epichains. Get epichains from <https://github.com/epiverse-trace/epichains>.Thank you for your support!

Core functionality

bpmodels provides three main functions:

chain_ll(): calculates the likelihoods of observing a vector of chains of given sizes or lengths.

Here is a quick example of estimating the loglikelihood of an observed chain:

# example of observed chain sizes
chain_sizes <- c(1, 2, 3, 4) 
# estimate loglikelihood of the observed chain sizes
chain_ll_eg <- chain_ll(x = chain_sizes, offspring = "pois", 
                        stat = "size", lambda = 0.5)
chain_ll_eg
#> [1] -7.772589

chain_sim(): simulates transmission chains until all chains stop producing offspring.

Below is a quick example where we simulate the chain sizes of $5$ chains with a poisson offspring with mean, $\text{lambda} = 0.5$:

set.seed(123)

chain_sim_eg <- chain_sim(n = 5, offspring = "pois", stat = "size", 
                          lambda = 0.5, tree = TRUE)

head(chain_sim_eg)
#>   n id ancestor generation
#> 1 1  1       NA          1
#> 2 2  1       NA          1
#> 3 3  1       NA          1
#> 4 4  1       NA          1
#> 5 5  1       NA          1
#> 6 2  2        1          2

chain_sim_susc(): simulates transmission chains from a specified population size with pre-existing immunity until the susceptible pool runs out.

Below is a quick example where we simulate chains with a poisson offspring with mean, $\text{lambda} = 0.5$, and serial interval of $3$:

set.seed(1234)

chain_sim_susc_eg <- chain_sim_susc(pop = 1000, offspring = "pois",
                                    mn_offspring = 0.5,
                                    serial = function(x) {3}
                                    )

head(chain_sim_susc_eg)
#>   id ancestor generation time
#> 1  1       NA          1    0

See the “Get started vignette” for a detailed illustration of each function.

Package vignettes

Specific use cases of bpmodels can be found in the online documentation as package vignettes, under “Articles”.

Reporting bugs

To report a bug please open an issue.

Contribute

We welcome contributions to enhance the package’s functionalities. If you wish to do so, please follow the package contributing guide.

Code of conduct

Please note that the bpmodels 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("bpmodels")
#> To cite package bpmodels in publications use:
#> 
#>   Sebastian Funk, Flavio Finger, and James M. Azam (2023). bpmodels:
#>   Analysing transmission chain statistics using branching process
#>   models, website: https://github.com/epiverse-trace/bpmodels/
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {bpmodels: Analysing transmission chain statistics using branching process models},
#>     author = {{Sebastian Funk} and {Flavio Finger} and {James M. Azam}},
#>     year = {2023},
#>     url = {https://github.com/epiverse-trace/bpmodels/},
#>   }