From 414ee017e0b3a5923ce3ca070c03ced34e0b8300 Mon Sep 17 00:00:00 2001 From: James Azam Date: Fri, 15 Mar 2024 13:07:21 +0000 Subject: [PATCH] Delete README.md to trigger render-readme --- README.md | 233 ------------------------------------------------------ 1 file changed, 233 deletions(-) delete mode 100644 README.md diff --git a/README.md b/README.md deleted file mode 100644 index b213ceaf..00000000 --- a/README.md +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - -# *epichains*: Methods for simulating and analysing the size and length of transmission chains from branching process models - - - -[![License: -MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) -[![R-CMD-check](https://github.com/epiverse-trace/epichains/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/epiverse-trace/epichains/actions/workflows/R-CMD-check.yaml) -[![codecov](https://codecov.io/github/epiverse-trace/epichains/branch/main/graphs/badge.svg)](https://codecov.io/github/epiverse-trace/epichains) -[![Lifecycle: -experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](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](https://github.com/epiverse-trace/bpmodels/) by providing -bespoke functions and data structures that allow easy manipulation and -interoperability with other Epiverse-TRACE packages, for example, -[superspreading](https://github.com/epiverse-trace/superspreading/) and -[epiparameter](https://github.com/epiverse-trace/epiparameter/), and -potentially some existing packages for handling transmission chains, for -example, [epicontacts](https://github.com/reconhub/epicontacts). - -*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 - -*epichains* provides two main functions: - -- `simulate_chains()`: simulates transmission chains using a simple - branching process model that accepts an index number of cases that - seed the outbreak, a distribution of offspring per case, and a chain - statistic to track (size or length/duration). It optionally accepts - other population related inputs such as the population size (defaults - to Inf) and percentage of the population initially immune (defaults to - 0). This function returns an object with columns that track - information on who infected whom, the generation of infection and, if - a generation time function is specified, the time of infection. - -- `simulate_summary()`: provides a performant version of - `simulate_chains()` that only tracks and return a vector of realized - chain sizes or lengths/durations for each index case without details - of the infection tree. - -- `likelihood()`: calculates the loglikelihood (or likelihood, depending - on the value of `log`) of observing a vector of transmission chain - sizes or lengths. - -The objects returned by the `simulate_*()` functions can be summarised -with `summary()`. Running `summary()` on the output of -`simulate_chains()` will return the same output as `simulate_summary()` -using the same inputs. - -Objects returned from `simulate_chains()` can be aggregated into a -`` of cases per time or generation with the function -`aggregate()`. The aggregated results can also be passed on to `plot()` -with its own arguments to customize the resulting plots. - -Each of the listed functionalities is demonstrated in detail in the -[“Getting Started” -vignette](https://epiverse-trace.github.io/epichains/articles/epichains.html). - -## Package vignettes - -The theory behind the models provided here can be found in the [theory -vignette](https://epiverse-trace.github.io/epichains/articles/theoretical_background.html). - -We have also collated a bibliography of branching process applications -in epidemiology. These can be found in the [literature -vignette](https://epiverse-trace.github.io/epichains/articles/branching_process_literature.html). - -Specific use cases of *epichains* can be found in the [online -documentation as package -vignettes](https://epiverse-trace.github.io/epichains/), under -“Articles”. - -## Related R packages - -As far as we know, below are the existing R packages for simulating -branching processes and transmission chains. - -- [bpmodels](https://github.com/epiverse-trace/bpmodels): provides - methods for analysing the size and length of transmission chains from - branching process models. `{epichains}` is intended to supersede - `{bpmodels}`. - -- [ringbp](https://github.com/epiforecasts/ringbp): a branching process - model, parameterised to the 2019-nCoV outbreak, and used to quantify - the potential effectiveness of contact tracing and isolation of cases. - -- [covidhm](https://github.com/biouea/covidhm): code for simulating - COVID-19 dynamics in a range of scenarios across a real-world social - network. The model is conceptually based on `{ringbp}`. - -- [epicontacts](https://github.com/reconhub/epicontacts): provides - methods for handling, analysing, and visualizing transmission chains - and contact-tracing data/linelists. - -- [simulist](https://epiverse-trace.github.io/simulist/): uses a - branching process model to simulate individual-level infectious - disease outbreak data, including line lists and contact tracing data. - This package is part of the Epiverse-TRACE Initiative. - -- [superspreading](https://epiverse-trace.github.io/superspreading/): - provides a set of functions to estimate and understand - individual-level variation in transmission of infectious diseases from - data on secondary cases. These are useful for understanding the role - of superspreading in the spread of infectious diseases and for - informing public health interventions. - -- [earlyR](https://github.com/reconhub/earlyR): estimates the - reproduction number (R), in the early stages of an outbreak. The model - requires a specified serial interval distribution, characterised by - the mean and standard deviation of the (Gamma) distribution, and data - on daily disease incidence, including only confirmed and probable - cases. - -- [projections](https://github.com/reconhub/projections): uses data on - daily incidence, the serial interval (time between onsets of infectors - and infectees) and the reproduction number to simulate plausible - epidemic trajectories and project future incidence. It relies on a - branching process where daily incidence follows a Poisson or a - Negative Binomial distribution governed by a force of infection. - -- [simulacr](https://github.com/reconhub/simulacr): simulates outbreaks - for specified values of reproduction number, incubation period, - duration of infectiousness, and optionally reporting delays. Outputs a - linelist stored as a `data.frame` with the class `outbreak`, including - information on transmission chains; the output can be converted to - `` objects for visualisation. - -- [outbreakr](https://sites.google.com/site/therepiproject/r-pac/outbreaker): - implements a Bayesian approach for reconstructing outbreak data from - pathogen genome sequences. It also implements a tool for outbreak - simulation. - -- [outbreakr2](https://github.com/reconhub/outbreaker2): a Bayesian - framework for integrating epidemiological and genetic data to - reconstruct transmission trees of densely sampled outbreaks. It - re-implements, generalises and replaces the model of outbreaker, and - uses a modular approach which enables fine customisation of priors, - likelihoods and parameter movements. - -- [o2geosocial](https://github.com/alxsrobert/o2geosocial): integrates - geographical and social contact data to reconstruct transmission - chains. It combines the age group, location, onset date and genotype - of cases to infer their import status, and their likely infector. - -- [nosoi](https://github.com/slequime/nosoi): simulates agent-based - transmission chains by taking into account the influence of multiple - variables on the transmission process (e.g. dual-host systems (such as - arboviruses), within-host viral dynamics, transportation, population - structure), alone or taken together, to create complex but relatively - intuitive epidemiological simulations. - -- [TransPhylo](https://xavierdidelot.github.io/TransPhylo/index.html): - reconstructs infectious disease transmission using genomic data. - -## 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: -#> -#> Azam J, Finger F, Funk S (2024). _epichains: Simulating and Analysing -#> Transmission Chain Statistics Using Branching Process Models_. R -#> package version 0.0.0.9999, -#> https://epiverse-trace.github.io/epichains/, -#> . -#> -#> A BibTeX entry for LaTeX users is -#> -#> @Manual{, -#> title = {epichains: Simulating and Analysing Transmission Chain Statistics Using -#> Branching Process Models}, -#> author = {James M. Azam and Flavio Finger and Sebastian Funk}, -#> year = {2024}, -#> note = {R package version 0.0.0.9999, -#> https://epiverse-trace.github.io/epichains/}, -#> url = {https://github.com/epiverse-trace/epichains}, -#> } -```