uofuepibio / epiworldrshiny

Shiny webapp for epiworldR package

epiworldRShiny: An RShiny Application for the epiworldR Package

This R package provides a user-friendly application for epiworldR, a wrapper of the C++ library epiworld. It provides a general framework for modeling disease transmission using agent-based models. Some of the main features include:

• Fast simulation with an average of 30 million agents/day per second.
• 9 different epidemiological models to choose from.
• Built-in capability for user-defined interventions.
• Built-in capability to define population and disease parameters.
• Informative visualizations and tables after running each simulation.

You can find more examples on the package’s website: https://uofuepibio.github.io/epiworldRShiny/

Installation

You can install the development version of epiworldRShiny from GitHub with:

devtools::install_github("UofUEpiBio/epiworldRShiny")

Or from CRAN

install.packages("epiworldRShiny")

To run this ShinyApp, you need to type the following:

epiworldRShiny::run_app()

Examples

Example #1

This first example demonstrates how to run the Shiny app, run a simulation, and observe results. Notice the sidebar contains many disease and model parameters that can be altered. Changing these parameters will affect the spread of the infectious disease in the simulated population. After running the simulation, a plot of the distribution of states over time, a plot of the disease’s reproductive number, a model summary, and a table of counts over time are displayed.

This example features: - SEIR network model for COVID-19
The day of peak infections occurs on day 12, maxing at about 18,000 infections.
- The disease spreads rapidly at the simulation’s beginning, drastically decreasing over the first ten days.
- Model summary
- State counts table

Example #2

This example features the implementation of the vaccine and school closure interventions to curb disease spread. All model output can be interpreted using the same logic from example #1.

Key features: - SEIRD network model for COVID-19
- Vaccine prevalence = 70%
- School closure prevalence = 50%
- Day of school closure implementation = 7
- Significantly decreased number of infections and deaths.
- The majority of the population recovered or was susceptible by day 30.

Example #3

The last example features the SEIR equity model. This model is unique because it accounts for demographic diversity in a population, such as race, gender, and age. This allows for comparing disease spread among different demographics, unlike the previous two examples.

Key features: - SEIR equity model for COVID-19 - 30% hispanic population, 70% non-hispanic - 52% female population - 30% of population younger than 20 years old - 30% of population between 20 and 60 years old - 40% of population older than 60.

epiworldrshiny's Issues

Address observed issues in R CMD check

• Add .github to .Rbuildignore https://github.com/UofUEpiBio/epiworldRShiny/actions/runs/7975962416/job/21775402094#step:7:69
• Use the epiworldRShiny_env instead of the Global Environment https://github.com/UofUEpiBio/epiworldRShiny/actions/runs/7975962416/job/21775402094#step:7:103
• Check title of R package on DESCRIPTION https://github.com/UofUEpiBio/epiworldRShiny/actions/runs/7975962416/job/21775402094#step:7:81
• Add README.Rmd to .Rbuildignore https://github.com/UofUEpiBio/epiworldRShiny/actions/runs/7975962416/job/21775402094#step:7:83

Create a docker image to simplify the deployment process

Because of the number of dependencies epiworldRShiny has, firing up the system to deployment on shinyapps.io takes too much time. A fix would be to keep a docker image we update ~1 month with all the dependencies to install epiworldRShiny.

Continuous integration: R CMD Check

@derekmeyer37, make sure to close this issue when the CI for R CMD check is up and running.

predict the timing and magnitude of a disease outbreak peak

Automatic deployment to ShinyApps.io using Golem and GitHub actions

NPI: Vaccines, Masking, school closures

This is based on the following user story:

As an Epidemiologist, I would like to add tools such as (vaccines, masking, school closures, etc.) so I can measure changes in disease spread due to public health interventions.

• Working slider for % of Vaccines.
• Working slider for % of Masking.
• Working slider for % of School closures.
• Define the proper language for each.

Issue with SEIR equity model

@gvegayon
There seems to be an issue with the equity model. I believe it has to do with this line in the model file (#42): epiworldRenv()$X <- X

Warning: Error in <-: invalid (NULL) left side of assignment
150: modelfun [/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/epiworldRShiny/models/shiny_seirconnequity.R#42]

I have not been able to figure it out. Take a look when you get a chance.

Checklist for CRAN release

Before sending to CRAN:

• The package CI (GitHub Actions) runs without errors, warnings, or notes in all OS.
• If there are any notes on R CMD check, those are OK (like 'size of the installed package').
• The version is 0.1-0 in the DESCRIPTION file.
• The description and title in the DESCRIPTION file are sound.
• No extraneous file is included in R CMD build (e.g., .DS_store or README.Rmd).
• The date in the DESCRIPTION file is updated.
• Double check grammar in the models' description (markdown files under inst).
• Make sure we have included the contributions from the Department of Health and Human Services.

cc @derekmeyer37

Predict the impact of an intervention on health inequities.

Add options for NPI

Currently, NPIs have defaults for various parameters. We need to make them optional and allow the user to specify them. Here are the places that need to be changed:

• GUI:

  epiworldRShiny/R/functions-ui.R

  Lines 187 to 222 in 2d85353

  	shiny::sliderInput(
  	inputId = paste0(model_name, "_vaccine_prevalence"),
  	label = "% of agents vaccinated",
  	min = 0,
  	max = 1,
  	value = 0,
  	step = 0.01,
  	ticks = FALSE
  	),
  	shiny::sliderInput(
  	inputId = paste0(model_name, "_masking_prevalence"),
  	label = "% of agents using masks",
  	value = "0",
  	min = 0,
  	max = 1,
  	step = 0.01,
  	ticks = FALSE
  	),
  	shiny::headerPanel(shiny::h4("School Closure")),
  	shiny::sliderInput(
  	inputId = paste0(model_name, "_school_closure_prevalence"),
  	label = "Prevalence",
  	value = "0",
  	min = 0,
  	max = 1,
  	step = 0.01,
  	ticks = FALSE
  	),
  	shiny::numericInput(
  	inputId = paste0(model_name, "_school_closure_day"),
  	label = "Implementation day",
  	value = "0",
  	min = 0,
  	max = 100,
  	step = 1
  	)

• Server side:

  epiworldRShiny/R/functions-npi.R

  Lines 132 to 158 in 2d85353

  	#' NPI adding function
  	#' @param model epiworldR model.
  	#' @param modelname Specified model.
  	#' @param input User epiworldR model selection.
  	#' @returns Returns an object of class epiworld_model, where model is
  	#' substituted with the model name.
  	#' @family npis
  	#' @export
  	npi_add_all <- function(model, modelname, input) {
  	npi_add_vaccine(
  	model = model,
  	preval = input[[paste0(modelname, "_vaccine_prevalence")]]
  	)
  	npi_add_masking(
  	model = model,
  	preval = input[[paste0(modelname, "_masking_prevalence")]]
  	)
  	npi_add_school_closure(
  	model = model,
  	preval = input[[paste0(modelname, "_school_closure_prevalence")]],
  	day = input[[paste0(modelname, "_school_closure_day")]]
  	)
  	}

Update README.Rmd featuring more of epiworldRShiny than epiworldR

We need the README.Rmd file to reflect the Shiny app, not the sim package. Here is the list of tasks:

• Provide a more thoughtful description of the package and its features.
• Add a couple of screenshots showing 2+ different models and outputs. If possible, a GIF showing the process from lunching to getting the results.
• Add code of conduct (using the usethis R package).
• Add a badge for the CI status (GitHub actions) like in epiworldR.

cc @derekmeyer37

Upload the README.Rmd file

It seems that the READM.Rmd file is missing from the package. Please add it to the tree (upload it).