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-# *epichains*: Methods for simulating and analysing the size and length of transmission chains from branching process models 
-
-
-
-
-[](https://github.com/epiverse-trace/epichains/actions/workflows/R-CMD-check.yaml)
-[](https://codecov.io/github/epiverse-trace/epichains)
-
-[](https://opensource.org/licenses/MIT)
-[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
-
-
-*epichains* is an R package to simulate, analyse, and visualize 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.
-
-*epichains* re-implements
-[bpmodels](%22https://github.com/epiverse-trace/bpmodels/%22) by
-providing dedicated data structures that allow easy manipulation and
-interoperability with other existing packages for handling transmission
-chain and contact-tracing data.
-
-*epichains* is developed at the [Centre for the Mathematical Modelling
-of Infectious
-Diseases](https://www.lshtm.ac.uk/research/centres/centre-mathematical-modelling-infectious-diseases)
-at the London School of Hygiene and Tropical Medicine as part of the
-[Epiverse Initiative](https://data.org/initiatives/epiverse/).
-
-# Installation
-
-The latest development version of the *epichains* package can be
-installed via
-
-``` r
-# check whether {pak} is installed
-if (!require("pak")) install.packages("pak")
-pak::pak("epiverse-trace/epichains")
-```
-
-To load the package, use
-
-``` r
-library("epichains")
-```
-
-# Quick start
-
-## Core functionality
-
-*epichains* provides four main functions:
-
-### [likelihood()](https://epiverse-trace.github.io/epichains/reference/likelihood.html)
-
-This function calculates the likelihood/loglikelihood of observing a
-vector of outbreak summaries obtained from transmission chains.
-Summaries here refer to transmission chain sizes or lengths/durations.
-
-`likelihood()` requires a vector of chain summaries (sizes or lengths),
-`chains`, the corresponding statistic to calculate, `statistic`, and the
-offspring distribution, `offspring_dist` its associated parameters. It
-also requires `nsim_obs`, which is the number of simulations to run if
-the likelihoods do not have a closed-form solution and must be
-simulated. This argument will be explained further in the [“Getting
-Started”](https://epiverse-trace.github.io/epichains/articles/epichains.html)
-vignette.
-
-Let’s look at the following example where we estimate the loglikelihood
-of observing `chain_sizes`.
-
-``` r
-set.seed(121)
-# example of observed chain sizes
-# randomly generate 20 chains of size between 1 to 10
-chain_sizes <- sample(1:10, 20, replace = TRUE)
-```
-
-``` r
-# estimate loglikelihood of the observed chain sizes
-likelihood_eg <- likelihood(
- chains = chain_sizes,
- statistic = "size",
- offspring_dist = "pois",
- nsim_obs = 100,
- lambda = 0.5
-)
-# Print the estimate
-likelihood_eg
-#> [1] -67.82879
-```
-
-### [simulate_tree()](https://epiverse-trace.github.io/epichains/reference/simulate_tree.html)
-
-`simulate_tree()` simulates an outbreak from a given number of
-infections. It retains and returns information on infectors (ancestors),
-infectees, the generation of infection, and the time, if a serial
-distribution is specified.
-
-Let’s look at an example where we simulate the transmission trees of
-$10$ initial infections/chains. We assume a poisson offspring
-distribution with mean, $\text{lambda} = 0.9$, and a serial interval of
-$3$ days:
-
-``` r
-set.seed(123)
-
-sim_tree_eg <- simulate_tree(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- stat_max = 10,
- serials_dist = function(x) 3,
- lambda = 0.9
-)
-
-head(sim_tree_eg)
-#> < tree head (from first known ancestor) >
-#> chain_id sim_id ancestor generation time
-#> 11 2 2 1 2 3
-#> 13 3 2 1 2 3
-#> 14 4 2 1 2 3
-#> 16 5 2 1 2 3
-#> 19 7 2 1 2 3
-#> 20 8 2 1 2 3
-```
-
-`simulate_tree()` can model population-level intervention by reducing
-the $R_0$, using the `intvn_mean_reduction` argument.
-
-To illustrate this, we will use the previous example and specify a
-population-level intervention that reduces $R_0$ by $50\%$.
-
-``` r
-set.seed(123)
-
-sim_tree_intvn_eg <- simulate_tree(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- intvn_mean_reduction = 0.5,
- stat_max = 10,
- serials_dist = function(x) 3,
- lambda = 0.9
-)
-
-head(sim_tree_intvn_eg)
-#> < tree head (from first known ancestor) >
-#> chain_id sim_id ancestor generation time
-#> 11 2 2 1 2 3
-#> 12 4 2 1 2 3
-#> 13 5 2 1 2 3
-#> 15 8 2 1 2 3
-#> 14 5 3 1 2 3
-#> 16 2 3 2 3 6
-```
-
-### [simulate_summary()](https://epiverse-trace.github.io/epichains/reference/simulate_summary.html)
-
-`simulate_summary()` is basically `simulate_tree()` except that it does
-not retain information on each infector and infectee. It returns the
-eventual size or length/duration of each transmission chain.
-
-Here is an example to simulate the previous examples without
-intervention, returning the size of each of the $10$ chains. It assumes
-a poisson offspring distribution with mean of $0.9$.
-
-``` r
-set.seed(123)
-
-simulate_summary_eg <- simulate_summary(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- stat_max = 10,
- lambda = 0.9
-)
-
-# Print the results
-simulate_summary_eg
-#> `epichains` object
-#>
-#> [1] 1 Inf 4 4 Inf 1 2 Inf 5 3
-#>
-#> Simulated chain sizes:
-#>
-#> Max: 5
-#> Min: 1
-```
-
-Here is an example with an intervention that reduces $R_0$ by $50\%$.
-
-``` r
-simulate_summary_intvn_eg <- simulate_summary(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- intvn_mean_reduction = 0.5,
- stat_max = 10,
- lambda = 0.9
-)
-
-# Print the results
-simulate_summary_intvn_eg
-#> `epichains` object
-#>
-#> [1] 1 1 1 1 1 2 1 1 2 1
-#>
-#> Simulated chain sizes:
-#>
-#> Max: 2
-#> Min: 1
-```
-
-### [simulate_tree_from_pop()](https://epiverse-trace.github.io/epichains/reference/simulate_tree_from_pop.html)
-
-`simulate_tree_from_pop()` simulates outbreaks based on a specified
-population size and pre-existing immunity until the susceptible pool
-runs out.
-
-Here is a quick example where we simulate an outbreak in a population of
-size $1000$. We assume individuals have a poisson offspring distribution
-with mean, $\text{lambda} = 1$, and serial interval of $3$:
-
-``` r
-set.seed(7)
-
-sim_tree_from_pop_eg <- simulate_tree_from_pop(
- pop = 1000,
- offspring_dist = "pois",
- lambda = 1,
- serials_dist = function(x) {3}
- )
-
-head(sim_tree_from_pop_eg)
-#> < tree head (from first known ancestor) >
-#> sim_id ancestor generation time
-#> 2 2 1 2 3
-#> 3 3 1 2 3
-#> 4 4 1 2 3
-#> 5 5 1 2 3
-#> 6 6 2 3 6
-#> 7 7 6 4 9
-```
-
-## Other functionalities
-
-### Summarising
-
-You can run `summary()` on `` objects to get useful
-summaries.
-
-``` r
-# Example with simulate_tree()
-set.seed(123)
-
-sim_tree_eg <- simulate_tree(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- stat_max = 10,
- serials_dist = function(x) 3,
- lambda = 0.9
-)
-
-summary(sim_tree_eg)
-#> $chains_run
-#> [1] 10
-#>
-#> $max_time
-#> [1] 12
-#>
-#> $unique_ancestors
-#> [1] 9
-#>
-#> $max_generation
-#> [1] 5
-
-# Example with simulate_summary()
-set.seed(123)
-
-simulate_summary_eg <- simulate_summary(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- stat_max = 10,
- lambda = 0.9
-)
-
-# Get summaries
-summary(simulate_summary_eg)
-#> $chains_run
-#> [1] 10
-#>
-#> $max_chain_stat
-#> [1] 5
-#>
-#> $min_chain_stat
-#> [1] 1
-```
-
-### Aggregating
-
-You can aggregate `` objects returned by the `simulate_*()`
-functions into a time series, which is a `` with columns
-“cases” and either “generation” or “time”, depending on the value of
-`grouping_var`.
-
-To aggregate over “time”, you must have specified a serial interval
-distribution in the simulation step.
-
-``` r
-# Example with simulate_tree()
-set.seed(123)
-
-sim_tree_eg <- simulate_tree(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- stat_max = 10,
- serials_dist = function(x) 3,
- lambda = 0.9
-)
-
-aggregate(sim_tree_eg, grouping_var = "time")
-#> time cases
-#> 1 0 10
-#> 2 3 13
-#> 3 6 15
-#> 4 9 18
-#> 5 12 2
-```
-
-### Plotting
-
-Aggregated `` objects can easily be plotted using base R or
-`ggplot2` with little to no data manipulation.
-
-Here is an end-to-end example from simulation through aggregation to
-plotting.
-
-``` r
-# Run simulation with simulate_tree()
-set.seed(123)
-
-sim_tree_eg <- simulate_tree(
- nchains = 10,
- statistic = "size",
- offspring_dist = "pois",
- stat_max = 10,
- serials_dist = function(x) 3,
- lambda = 0.9
-)
-
-# Aggregate cases over time
-sim_aggreg <- aggregate(sim_tree_eg, grouping_var = "time")
-
-# Plot cases over time
-plot(sim_aggreg, type = "b")
-```
-
-
-
-## Package vignettes
-
-Specific use cases of *epichains* can be found in the [online
-documentation as package
-vignettes](https://epiverse-trace.github.io/epichains/), under
-“Articles”.
-
-## Reporting bugs
-
-To report a bug please open an
-[issue](https://github.com/epiverse-trace/epichains/issues/new/choose).
-
-## Contribute
-
-We welcome contributions to enhance the package’s functionalities. If
-you wish to do so, please follow the [package contributing
-guide](https://github.com/epiverse-trace/epichains/blob/main/.github/CONTRIBUTING.md).
-
-## Code of conduct
-
-Please note that the *epichains* project is released with a [Contributor
-Code of
-Conduct](https://github.com/epiverse-trace/.github/blob/main/CODE_OF_CONDUCT.md).
-By contributing to this project, you agree to abide by its terms.
-
-## Citing this package
-
-``` r
-citation("epichains")
-#> To cite package epichains in publications use:
-#>
-#> Sebastian Funk, Flavio Finger, and James M. Azam (2023). epichains:
-#> Analysing transmission chain statistics using branching process
-#> models, website: https://github.com/epiverse-trace/epichains/
-#>
-#> A BibTeX entry for LaTeX users is
-#>
-#> @Manual{,
-#> title = {epichains: 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/epichains/},
-#> }
-```