[{"_1":2,"_152":-5,"_153":-5},"loaderData",{"_3":4,"_13":14,"_20":21,"_131":132},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","routes/_layout.r.$namespace.$reponame",{"_22":23,"_24":-5,"_25":11,"_26":11,"_27":28,"_29":30,"_31":-5,"_32":11,"_33":34,"_83":-5,"_84":85,"_124":-5,"_125":11,"_126":17,"_127":11,"_128":-5,"_129":130},"canonicalUrl","https://hub.docker.com/r/seabbs/idmodelr/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","seabbs","reponame","idmodelr","cloudBuildSource","isAutobuildsAvailable","repo",{"_35":36,"_37":38,"_40":41,"_42":43,"_44":11,"_45":46,"_47":48,"_49":50,"_52":30,"_27":28,"_53":54,"_55":39,"_56":57,"_58":59,"_60":61,"_62":-5,"_63":57,"_64":65,"_66":11,"_67":28,"_68":69,"_74":17,"_75":76,"_80":28,"_81":82},"categories",[],"content_types",[39],"image","date_registered","2017-10-18T18:52:29.231803Z","description","Utility Functions for Infectious Disease Modelling","is_private","last_updated","2021-07-29T18:01:56.065496Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[51],"application/vnd.docker.container.image.v1+json","name","pull_count",892,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","\n\n\n# Infectious disease model library and utilities $\"\"$ \n\n[![badge](https://img.shields.io/badge/Launch-idmodelr-lightblue.svg)](https://mybinder.org/v2/gh/seabbs/idmodelr/master?urlpath=rstudio)\n[![CRAN\\_Status\\_Badge](http://www.r-pkg.org/badges/version/idmodelr)](https://cran.r-project.org/package=idmodelr)\n[![develVersion](https://img.shields.io/badge/devel%20version-0.3.2-blue.svg?style=flat)](https://github.com/seabbs/idmodelr)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2641048.svg)](https://doi.org/10.5281/zenodo.2641048)\n[![metacran monthly\ndownloads](http://cranlogs.r-pkg.org/badges/idmodelr)](https://cran.r-project.org/package=idmodelr)\n[![metacran\ndownloads](http://cranlogs.r-pkg.org/badges/grand-total/idmodelr?color=ff69b4)](https://cran.r-project.org/package=idmodelr)\n\nExplore a range of infectious disease models in a consistent framework.\nThe primary aim of `idmodelr` is to provide a library of infectious\ndisease models for researchers, students, and other interested\nindividuals. These models can be used to understand the underlying\ndynamics and as a reference point when developing models for research.\n`idmodelr` also provides a range of utilities. These include: plotting\nfunctionality; a simulation wrapper; scenario analysis tooling; an\ninteractive dashboard; tools for handling mult-dimensional models; and\nboth model and parameter look up tables. Unlike other modelling packages\nsuch as [`pomp`](https://kingaa.github.io/pomp/),\n[`libbi`](http://libbi.org) and [`EpiModel`](http://www.epimodel.org),\n`idmodelr` serves primarily as an educational resource. It is most\ncomparable to\n[`epirecipes`](http://epirecip.es/epicookbook/chapters/simple) but\nprovides a more consistent framework, an `R` based workflow, and\nadditional utility tooling. After users have explored model dynamics\nwith `idmodelr` they may then implement their model using one of these\npackages in order to utilise the model fitting tools they provide. For\nnewer modellers, this package reduces the barrier to entry by containing\nmultiple infectious disease models, providing a consistent framework for\nsimulation and visualisation, and\n[signposting](https://www.samabbott.co.uk/idmodelr/articles/resources.html)\ntowards other, more research, focussed resources.\n\n## Installation\n\nInstall the CRAN version:\n\n``` r\ninstall.packages(\"idmodelr\")\n```\n\nAlternatively install the development version from GitHub:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"seabbs/idmodelr\")\n```\n\n## Documentation\n\n[![Documentation](https://img.shields.io/badge/Documentation-release-lightgrey.svg?style=flat)](https://www.samabbott.co.uk/idmodelr/)\n[![Development\ndocumentation](https://img.shields.io/badge/Documentation-development-lightblue.svg?style=flat)](https://www.samabbott.co.uk/idmodelr/dev)\n[![Getting\nstarted](https://img.shields.io/badge/Documentation-getting%20started-yellow.svg?style=flat)](https://www.samabbott.co.uk/idmodelr)\n[![Functions](https://img.shields.io/badge/Documentation-functions-orange.svg?style=flat)](https://www.samabbott.co.uk/idmodelr/reference/index.html)\n\n## Testing\n\n[![Travis-CI Build\nStatus](https://travis-ci.org/seabbs/idmodelr.svg?branch=master)](https://travis-ci.org/seabbs/idmodelr)\n[![AppVeyor Build\nStatus](https://ci.appveyor.com/api/projects/status/github/seabbs/idmodelr?branch=master&svg=true)](https://ci.appveyor.com/project/seabbs/idmodelr)\n[![Coverage\nStatus](https://img.shields.io/codecov/c/github/seabbs/idmodelr/master.svg)](https://codecov.io/github/seabbs/idmodelr?branch=master)\n\n## Quick start\n\nIn this quick start guide we are going to be defining, simulating and\nplotting a Susceptible-Infected-Recovered deterministic compartmental\nmodel with simple population demographics (births = deaths). The first\nstep is to load the `idmodelr` package.\n\n``` r\nlibrary(idmodelr)\n```\n\nThe next step is to find the model of interest amongst those implemented\nin `idmodelr`. `model_details` lists all of the models implemented in\n`idmodelr` and can be search using `dplyr`, base R, or other dataframe\ntools.\n\n``` r\nlibrary(dplyr)\n#> \n#> Attaching package: 'dplyr'\n#> The following objects are masked from 'package:stats':\n#> \n#> filter, lag\n#> The following objects are masked from 'package:base':\n#> \n#> intersect, setdiff, setequal, union\n\nmodel_details %>% \n dplyr::filter(model_family %in% \"SIR\") %>% \n knitr::kable()\n```\n\n| model | model\\_family | time | type | recovered | exposed | treated | susceptible | risk\\_stratified | non\\_exponential | simple\\_demographics | vaccination | disease\\_example | language | parameters |\n| :---------------------------------- | :------------ | :--------- | :------------ | :-------- | :------ | :------ | :---------- | :--------------- | :--------------- | :------------------- | :---------- | :--------------- | :------- | :---------------------------------------- |\n| SIR\\_ode | SIR | continuous | deterministic | no | no | no | no | no | no | no | no | none | R | c(“beta”, “tau”) |\n| SIR\\_demographics\\_ode | SIR | continuous | deterministic | no | no | no | no | no | no | yes | no | none | R | c(“beta”, “tau”, “mu”) |\n| SIR\\_vaccination\\_ode | SIR | continuous | deterministic | no | no | no | no | no | no | no | yes | none | R | c(“beta”, “tau”, “lambda”) |\n| SIR\\_vaccination\\_demographics\\_ode | SIR | continuous | deterministic | no | no | no | no | no | no | yes | yes | none | R | c(“beta”, “tau”, “lambda”, “alpha”, “mu”) |\n\nNow look at the model and the model help file (`?SIR_demographics_ode`)\nto get an understanding of how the model is constructed.\n\n``` r\nSIR_demographics_ode\n#> function(t, x, params) {\n#> \n#> ## Specify model compartments\n#> S <- x[1]\n#> I <- x[2]\n#> R <- x[3]\n#> \n#> with(as.list(params),{\n#> \n#> ## Specify total population\n#> N = S + I + R\n#> \n#> ## Derivative Expressions\n#> dS = - beta * S * I / N - mu * S + mu * N\n#> dI = beta * S * I / N - tau * I - mu * I\n#> dR = tau * I - mu * R\n#> \n#> ## output\n#> derivatives <- c(dS, dI, dR)\n#> \n#> list(derivatives)\n#> })\n#> }\n#> \n#> \n```\n\nCheck the parameters required by the model using `required_parameters`.\nThis returns a table containing all the parameters that must be defined\nin order to use the model as well as descriptive information for each\nparameter.\n\n``` r\nparameters <- required_parameters(\"SIR_demographics_ode\")\n\nknitr::kable(parameters)\n```\n\n| parameter | parameter\\_family | description | type | risk\\_stratified | non\\_exponential |\n| :-------- | :---------------- | :------------------------------------------------------------------------------------------------------------------------ | :--- | :--------------- | :--------------- |\n| beta | transmission | Transmission rate = the transmission probability per contact \\* the number of contacts each individual has. | rate | no | no |\n| tau | recovery | Recovery rate. The reciprocal of the time infectious. | rate | no | no |\n| mu | demographics | The natural mortality rate. The reciprocal of the average lifespan. (for simple demographics this is also the birth rate. | rate | no | no |\n\nParameterise the model.\n\n``` r\nparameters <- data.frame(\n beta = 3, ##Transmission rate = contact rate * transmission probablity\n tau = 0.5, ## Rate recovcery = 1 / duration of infection\n mu = 1/81 ## Natural birth/death rate = 1 / average lifespan\n)\n```\n\nCheck the initial conditions required by looking at the start of the\nmodel function. In most cases this should match up to the model name\n(i.e S, I and R for an SIR model) but risk stratification etc. will\nrequire additional compartments.\n\n``` r\ninits <- data.frame(\n S = 999,\n I = 1,\n R = 0\n )\n```\n\nSpecify the timespan over which to run the model.\n\n``` r\ntimes <- seq(0, 50, 0.1)\n```\n\nSimulate the model.\n\n``` r\ntraj <- simulate_model(model = SIR_demographics_ode,\n sim_fn = solve_ode, ##as solving an ode\n inits = inits,\n params = parameters,\n times = times)\n\n\ntraj\n#> # A tibble: 501 x 4\n#> time S I R\n#> \n#> 1 0 999 1 0 \n#> 2 0.1 999. 1.28 0.0567\n#> 3 0.2 998. 1.64 0.129 \n#> 4 0.3 998. 2.11 0.222 \n#> 5 0.4 997. 2.70 0.342 \n#> 6 0.5 996. 3.46 0.494 \n#> 7 0.6 995. 4.43 0.690 \n#> 8 0.7 993. 5.67 0.940 \n#> 9 0.8 991. 7.25 1.26 \n#> 10 0.9 989. 9.28 1.67 \n#> # … with 491 more rows\n```\n\nSummarise the model.\n\n``` r\nsummarise_model(traj) %>% \n knitr::kable()\n```\n\n| Final size: S | Final size: I | Final size: R | Epidemic peak time | Epidemic peak | Epidemic duration |\n| ------------: | ------------: | ------------: | -----------------: | ------------: | ----------------: |\n| 136 | 31 | 833 | 3.5 | 533 | Inf |\n\nPlot the model trajectory.\n\n``` r\nplot_model(traj, facet = FALSE)\n```\n\n![](man/figures/unnamed-chunk-11-1.png)\n\nVary the model parameters, by increasing the mortality rate, and then\nsimulate the updated model.\n\n``` r\nparameters_up <- parameters\nparameters_up[[\"mu\"]] <- 1 / 20\n\n\ntraj_up <- simulate_model(model = SIR_demographics_ode,\n sim_fn = solve_ode,\n inits, \n parameters_up,\n times)\n```\n\nPlot the original trajectory and the updated trajectory. What has the\nimpact of increasing mortality been?\n\n``` r\nplot_model(traj, traj_up, facet = TRUE)\n```\n\n![](man/figures/unnamed-chunk-13-1.png)\n\nSee the package vignettes for more help getting started and some\nadditional ideas for exploring infectious disease model dynamics.\n\n## Dashboard\n\nA shiny application has been developed that showcases some of the\nfunctionality of the `idmodelr` package. This application allows the\nparameter spaces of a range of models built into `idmodelr` to be\nexplored in an interactive session. It is designed to be used as a\nteaching aid when introducing people to the concepts behind infectious\ndisease models without requiring them to interact with the underlying\ncode. The code for the dashboard can be found\n[here](https://github.com/seabbs/exploreidmodels). It can be run locally\nusing the following (*Note: this will install required packages to your\nsystem*),\n\n``` r\n#install.packages(\"shiny\")\nshiny::runGitHub(\"exploreidmodels\", \"seabbs\")\n```\n\n![Snapshot of the integrated\ndashboard.](man/figures/exploreidmodels.png)\n\n## Contributing\n\n### Contributing a model\n\nAdditional models are extremely welcome\\!\n\nTo add models in the same family as those already implemented (i.e\n[`SIR_ode`](https://github.com/seabbs/idmodelr/blob/master/R/SIR.R))\nplease follow the implemented coding style closely (alternatively open\nan [issue](https://github.com/seabbs/idmodelr/issues) explaining why\nthis style needs updating). Models should be named using their\ncompartments in capitals followed by lower case model details, and\nfinally the model type. An example of this is the `SIR_demographics_ode`\nmodel. For highly complex models only the most major model details\nshould be listed (aim for less than 40 characters). An entry for\n`model_details` is also required (see\n[`model_details.R`](https://github.com/seabbs/idmodelr/blob/master/data-raw/model_details.R)).\nIf new parameters have been used then a description must be added to\n`parameter_details` (see\n[`parameter_details.R`](https://github.com/seabbs/idmodelr/blob/master/data-raw/parameter_details.R)).\nPlease consider also adding to the testing suite for your new model (or\nflagging the lack of tests). Models can either be added via a pull\nrequest or via an issue.\n\nTo add a new family of models (i.e stochastic models) please open an\n[issue](https://github.com/seabbs/idmodelr/issues) outlining your\nproposed approach. A new family of models is likely to require at least\nits own `solve_` (equivalent to\n[`solve_ode`](https://github.com/seabbs/idmodelr/blob/master/R/solve.R))\nfunction and may also require other package changes. Models implemented\nin other languages (i.e C) are also very welcome.\n\n### Other contributions\n\nFile an issue [here](https://github.com/seabbs/idmodelr/issues) if there\nis any other feature, that you think is missing from the package, or\nbetter yet submit a pull request\\!\n\nPlease note that the `idmodelr` project is released with a [Contributor\nCode of\nConduct](https://github.com/seabbs/idmodelr/blob/master/.github/CODE_OF_CONDUCT.md).\nBy contributing to this project, you agree to abide by its terms.\n\n### Docker\n\nThis packge was developed in a docker container based on the\n[tidyverse](https://hub.docker.com/r/rocker/tidyverse/) docker image. To\nrun the docker image\nrun:\n\n``` bash\ndocker run -d -p 8787:8787 --name idmodelr -e USER=seabbs -e PASSWORD=seabbs seabbs/idmodelr\n```\n\nThe rstudio client can be found on port `:8787` at your local machines\nip. The default username:password is seabbs:seabbs, set the user with\n`-e USER=username`, and the password with `- e\nPASSWORD=newpasswordhere`. The default is to save the analysis files\ninto the user directory. Alternatively, access the development\nenvironment via\n[binder](https://mybinder.org/v2/gh/seabbs/idmodelr/master?urlpath=rstudio).\n","has_starred","hub_user","immutable_tags_settings",{"_70":11,"_71":72},"enabled","rules",[73],".*","is_automated","permissions",{"_77":17,"_78":11,"_79":11},"read","write","admin","user","storage_size",625825942881,"orgData","recentTags",[86],{"_87":39,"_88":89,"_90":91,"_92":93,"_94":95,"_45":113,"_114":89,"_115":28,"_116":51,"_52":117,"_118":119,"_120":104,"_121":113,"_122":61,"_123":17},"content_type","creator",1943981,"full_size",1688395390,"id",16758351,"images",[96],{"_97":98,"_99":100,"_101":102,"_103":104,"_105":106,"_107":108,"_109":102,"_110":-5,"_111":91,"_58":61,"_112":-5},"architecture","amd64","digest","sha256:726fcfa23b5c6ccde8e91f0e6c5659ca308664bb34af15982a1bc503e868b139","features","","last_pulled","2026-09-14T08:13:51.383160193Z","last_pushed","2021-07-29T18:01:56Z","os","linux","os_features","os_version","size","variant","2021-07-29T18:01:55.671748Z","last_updater","last_updater_username","media_type","latest","repository",1949068,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_33":133,"_140":141,"_129":150,"_151":11},{"_35":134,"_37":135,"_40":41,"_42":43,"_44":11,"_45":46,"_47":48,"_49":136,"_52":30,"_27":28,"_53":54,"_55":39,"_56":57,"_58":59,"_60":61,"_62":-5,"_63":57,"_64":65,"_66":11,"_67":28,"_68":137,"_74":17,"_75":139,"_80":28,"_81":82},[],[39],[51],{"_70":11,"_71":138},[73],{"_77":17,"_78":11,"_79":11},"tags",{"_142":59,"_143":-5,"_144":-5,"_145":146},"count","next","previous","results",[147],{"_87":39,"_88":89,"_90":91,"_92":93,"_94":148,"_45":113,"_114":89,"_115":28,"_116":51,"_52":117,"_118":119,"_120":104,"_121":113,"_122":61,"_123":17},[149],{"_97":98,"_99":100,"_101":102,"_103":104,"_105":106,"_107":108,"_109":102,"_110":-5,"_111":91,"_58":61,"_112":-5},[],"canDeleteTags","actionData","errors"]

seabbs/idmodelr

Tags for seabbs/idmodelr