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Addressing CRAN comments
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‎DESCRIPTION

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Package: epiworldRShiny
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Type: Package
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Title: An 'RShiny' Wrapper of the R Package 'epiworldR'
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Title: A 'shiny' Wrapper of the R Package 'epiworldR'
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Version: 0.1-0
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Date: 2024-05-14
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Date: 2024-05-31
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Authors@R: c(
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person("Derek", "Meyer", role=c("aut","cre"),
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email="derekmeyer37@gmail.com", comment = c(ORCID = "0009-0005-1350-6988")),
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person("Matthew", "Samore", role=c("ctb"), email="matthew.samore@hsc.utah.edu", comment = c(ORCID = "0000-0002-4862-9196")),
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person("Jay", "Love", role=c("ctb"), email="jay.love@utah.edu", comment = c(ORCID="0000-0002-9371-2466")),
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person("Kristina", "Stratford", role=c("ctb"), email="kristina.stratford@hsc.utah.edu"))
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Description: This 'RShiny' wrapper provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and the Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.
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Description: R 'shiny' web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package 'epiworldR' (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. 'epiworldRShiny' provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.
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URL: https://github.com/UofUEpiBio/epiworldRShiny/,
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https://uofuepibio.github.io/epiworldRShiny/,
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BugReports: https://github.com/UofUEpiBio/epiworldRShiny/issues/

‎R/app.R

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#' @importFrom stats aggregate as.formula reshape
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#' @importFrom utils write.csv packageVersion
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#'
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#' @return Loads and opens the RShiny app for the epiworldR package
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#' @return Loads and opens the R shiny app for the epiworldR package
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#' @param ... Currently ignored.
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#' @export
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#' @name epiworldRShiny

‎R/functions-ui.R

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#' @rdname epiworldrshiny-ui
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#' @export
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#' @examples
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#' # slider_prevalence("SEIRD")
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#' slider_prevalence("SEIRD")
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slider_prevalence <- function(model_name) {
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shiny::sliderInput(
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paste0(model_name, "_prevalence"),

‎README.Rmd

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---
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title: "epiworldRShiny: An RShiny Application for the epiworldR Package"
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title: "epiworldRShiny: A 'shiny' Wrapper of the R Package 'epiworldR'"
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output: github_document
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---
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‎README.md

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epiworldRShiny: An RShiny Application for the epiworldR Package
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epiworldRShiny: A ‘shiny’ Wrapper of the R Package ‘epiworldR’
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================
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<!-- badges: start -->
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<!-- badges: end -->
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This R package provides a user-friendly application for
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[epiworldR](https://github.com/UofUEpiBio/epiworldR), a wrapper of the
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C++ library [epiworld](https://github.com/UofUEpiBio/epiworld). It
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provides a general framework for modeling disease transmission using
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[agent-based
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models](https://en.wikipedia.org/w/index.php?title=Agent-based_model&oldid=1153634802).
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Some of the main features include:
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- Fast simulation with an average of 30 million agents/day per second.
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- 9 different epidemiological models to choose from.
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- Built-in capability for user-defined interventions.
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- Built-in capability to define population and disease parameters.
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- Informative visualizations and tables after running each simulation.
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<a href="https://github.com/UofUEpiBio/epiworldR"
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target="_blank">epiworldR</a>, a wrapper of the C++ library
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<a href="https://github.com/UofUEpiBio/epiworld"
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target="_blank">epiworld</a>. It provides a general framework for
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modeling disease transmission using <a
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href="https://en.wikipedia.org/w/index.php?title=Agent-based_model&amp;oldid=1153634802"
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target="_blank">agent-based models</a>. Some of the main features
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include:
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- Fast simulation with an average of 30 million agents/day per second.
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- 9 different epidemiological models to choose from.
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- Built-in capability for user-defined interventions.
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- Built-in capability to define population and disease parameters.
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- Informative visualizations and tables after running each simulation.
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You can find more examples on the package’s website:
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<https://uofuepibio.github.io/epiworldRShiny/>
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This example features: - SEIR network model for COVID-19
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The day of peak infections occurs on day 12, maxing at about 18,000
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infections.
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\- The disease spreads rapidly at the simulation’s beginning,
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drastically decreasing over the first ten days.
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\- Model summary
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\- State counts table
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![example 1
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GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/f4e7d313-e3b6-4ebb-9c0a-ca4d53ef9cea)
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- The disease spreads rapidly at the simulation’s beginning, drastically
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decreasing over the first ten days.
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- Model summary
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- State counts table
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<figure>
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<img
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src="https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/f4e7d313-e3b6-4ebb-9c0a-ca4d53ef9cea"
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alt="example 1 GIF" />
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<figcaption aria-hidden="true">example 1 GIF</figcaption>
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</figure>
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### Example \#2
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interpreted using the same logic from example \#1.
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Key features: - SEIRD network model for COVID-19
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\- Vaccine prevalence = 70%
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\- School closure prevalence = 50%
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\- Day of school closure implementation = 7
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\- Significantly decreased number of infections and deaths.
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\- The majority of the population recovered or was susceptible by day
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30.
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![example 2
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GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/d5405162-f7fe-4a42-8a4c-e9a2ac31be73)
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- Vaccine prevalence = 70%
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- School closure prevalence = 50%
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- Day of school closure implementation = 7
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- Significantly decreased number of infections and deaths.
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- The majority of the population recovered or was susceptible by day 30.
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<figure>
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<img
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src="https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/d5405162-f7fe-4a42-8a4c-e9a2ac31be73"
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alt="example 2 GIF" />
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<figcaption aria-hidden="true">example 2 GIF</figcaption>
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</figure>
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### Example \#3
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younger than 20 years old - 30% of population between 20 and 60 years
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old - 40% of population older than 60.
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![example 3
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GIF](https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/20aeb62d-cb42-4882-8577-a3406f167bca)
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<figure>
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<img
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src="https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/20aeb62d-cb42-4882-8577-a3406f167bca"
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alt="example 3 GIF" />
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<figcaption aria-hidden="true">example 3 GIF</figcaption>
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</figure>

‎man/epiworldRShiny.Rd

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‎man/epiworldrshiny-ui.Rd

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