| Title: | A 'shiny' Wrapper of the R Package 'epiworldR' |
|---|---|
| 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. |
| Authors: | Derek Meyer [aut, cre]  ,
George Vega Yon [aut] ,
Centers for Disease Control and Prevention [fnd] (Award number
1U01CK000585; 75D30121F00003),
Lindsay Keegan [ctb] ,
Karim Khader [ctb] ,
Damon Toth [ctb] ,
Randon Gruninger [ctb],
Matthew Samore [ctb] ,
Jay Love [ctb] ,
Kristina Stratford [ctb] |
| Maintainer: | Derek Meyer <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1-0 |
| Built: | 2024-10-29 06:17:55 UTC |
| Source: | https://github.com/uofuepibio/epiworldrshiny |
This function is for internal use only.
epiworldRenv()epiworldRenv()
Returns the epiworldR_env environment.
Fires up the R Shiny App. You can find more examples and documentation at the package's website: https://UofUEpiBio.github.io/epiworldRShiny/.
run_app is a wrapper for the epiworldRShiny function. It is a
convenience function to run the app.
epiworldRShiny(...) run_app(...)epiworldRShiny(...) run_app(...)
... |
Currently ignored. |
Loads and opens the R shiny app for the epiworldR package
All functions in this section are for internal use only. They are used to build the UI for the epiworldRShiny app.
text_input_disease_name(model_name) slider_prevalence(model_name) numeric_input_ndays(model_name) slider_input_rate(model_name, rate_name, value, maxval = 1, input_label = NULL) network_input(model_name) npis_input(model_name) seed_input(model_name) models_setup() population_input(model_name) simulate_button(model_name)text_input_disease_name(model_name) slider_prevalence(model_name) numeric_input_ndays(model_name) slider_input_rate(model_name, rate_name, value, maxval = 1, input_label = NULL) network_input(model_name) npis_input(model_name) seed_input(model_name) models_setup() population_input(model_name) simulate_button(model_name)
model_name |
Name of the epiworldR model. |
rate_name |
Name of the rate. |
value |
Initial value for the slider. |
maxval |
Maxiumum value for the slider. |
input_label |
Aids in creating the appropriate slider name. |
Unless otherwise specified, returns an object of class shiny.tag.
network_input returns an object of class shiny::tagList (shiny.tag.list).
npis_input returns an object of class shiny::tagList (shiny.tag.list).
models_setup returns an object of class list.
population_input returns an object of class shiny.tag.list.
text_input_disease_name("SEIRD") slider_prevalence("SEIRD") numeric_input_ndays("SEIRD") slider_input_rate("SEIRD", "transmission", value = 0.3, maxval = 1, input_label = NULL) network_input("SEIRD") npis_input("SEIRD") seed_input("SEIRD") models_setup() population_input("SEIRD") simulate_button("SEIRD")text_input_disease_name("SEIRD") slider_prevalence("SEIRD") numeric_input_ndays("SEIRD") slider_input_rate("SEIRD", "transmission", value = 0.3, maxval = 1, input_label = NULL) network_input("SEIRD") npis_input("SEIRD") seed_input("SEIRD") models_setup() population_input("SEIRD") simulate_button("SEIRD")
This function determines the scale of the y-axis for plot_epi.
find_scale(x)find_scale(x)
x |
The maximum value found in the model state counts |
An integer representing the scale for the y-axis. A max counts value of 10000 will return a scale of 1, 100000 will return a scale of 1000, 1000000 will return a scale of 10000.
Other Server side functions:
plot_epi(),
plot_reproductive_epi(),
pop_generator()
find_scale(100000)find_scale(100000)
NPI adding function
interventions_add_all(model, modelname, input)interventions_add_all(model, modelname, input)
model |
epiworldR model. |
modelname |
Specified model. |
input |
User epiworldR model selection. |
Returns an object of class epiworld_model, where model is substituted with the model name.
Other interventions:
npi_add_masking(),
npi_add_school_closure(),
pi_add_vaccine()
Masking intervention (non-pharmaceutical intervention)
npi_add_masking(model, preval, transmission_reduction)npi_add_masking(model, preval, transmission_reduction)
model |
epiworldR model. |
preval |
Prevalence of masking within the population. |
transmission_reduction |
Reduction in transmission probability due to masking |
Returns an object of class epiworld_model, where model is substituted with the model name.
Other interventions:
interventions_add_all(),
npi_add_school_closure(),
pi_add_vaccine()
library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) npi_add_masking(model, preval = .8, transmission_reduction = .7)library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) npi_add_masking(model, preval = .8, transmission_reduction = .7)
School closure intervention (non-pharmaceutical intervention)
npi_add_school_closure(model, preval, day, transmission_reduction)npi_add_school_closure(model, preval, day, transmission_reduction)
model |
epiworldR model. |
preval |
Prevalence of school closure within the population. |
day |
Day in the simulation where school closure goes into effect. |
transmission_reduction |
Reduction in transmission probability due to school closure. |
Returns an object of class epiworld_model, where model is substituted with the model name.
Other interventions:
interventions_add_all(),
npi_add_masking(),
pi_add_vaccine()
library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) npi_add_school_closure(model, preval = .8, transmission_reduction = .3, day = 10)library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) npi_add_school_closure(model, preval = .8, transmission_reduction = .3, day = 10)
Vaccination (pharmaceutical intervention)
pi_add_vaccine( model, preval, susceptibility_reduction, transmission_reduction, recovery_enhancer, death_reduction )pi_add_vaccine( model, preval, susceptibility_reduction, transmission_reduction, recovery_enhancer, death_reduction )
model |
epiworldR model. |
preval |
Initial prevalence of the vaccine. |
susceptibility_reduction |
Reduction in susceptibility probability due to vaccine. |
transmission_reduction |
Reduction in transmission probability due to vaccine. |
recovery_enhancer |
Probability increase in recovery due to vaccine. |
death_reduction |
Reduction in death probability due to vaccine. |
Returns an object of class epiworld_model, where model is substituted with the model name.
Other interventions:
interventions_add_all(),
npi_add_masking(),
npi_add_school_closure()
library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) pi_add_vaccine(model, preval = .8, susceptibility_reduction = .9, transmission_reduction = .5, recovery_enhancer = .5, death_reduction = .9)library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) pi_add_vaccine(model, preval = .8, susceptibility_reduction = .9, transmission_reduction = .5, recovery_enhancer = .5, death_reduction = .9)
This function generates a plot of the model states over time
plot_epi(model, mark_max)plot_epi(model, mark_max)
model |
The number of individuals in the population. |
mark_max |
The state which will have a mark at the peak |
A plot displaying each state from the model over the course of the simulation
Other Server side functions:
find_scale(),
plot_reproductive_epi(),
pop_generator()
library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) plot_epi(model, mark_max = "Infected")library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) plot_epi(model, mark_max = "Infected")
This function generates a plot of the reproductive number over time
plot_reproductive_epi(model)plot_reproductive_epi(model)
model |
The model object |
A plot displaying the reproductive number for the model over the course of the simulation
Other Server side functions:
find_scale(),
plot_epi(),
pop_generator()
library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) plot_reproductive_epi(model)library(epiworldR) # for ModelSEIRCONN function model <- ModelSEIRCONN("COVID-19", n = 1000, prevalence = 0.05, contact_rate = 4, transmission_rate = 0.1, incubation_days = 7, recovery_rate = 0.14) run(model, ndays = 100, seed = 123) plot_reproductive_epi(model)
This function generates a population matrix with specified characteristics.
pop_generator( n, prop_hispanic = 0.5, prop_female = 0.5, prop_19_59_60plus = c(0.3, 0.6) )pop_generator( n, prop_hispanic = 0.5, prop_female = 0.5, prop_19_59_60plus = c(0.3, 0.6) )
n |
The number of individuals in the population. |
prop_hispanic |
The proportion of individuals who are Hispanic. Default is 0.5. |
prop_female |
The proportion of individuals who are female. Default is 0.5. |
prop_19_59_60plus |
A vector of length 3 representing the proportions of individuals in the age groups 0-19, 20-59, and 60+. Default is c(0.5, 0.3, 0.2). |
A matrix representing the generated population, with columns for age groups (0-19, 20-59, 60+), NotHispanic, and Female.
Other Server side functions:
find_scale(),
plot_epi(),
plot_reproductive_epi()
pop_generator(n = 1000, prop_hispanic = .5, prop_female = .5, prop_19_59_60plus = c(.3, .6))pop_generator(n = 1000, prop_hispanic = .5, prop_female = .5, prop_19_59_60plus = c(.3, .6))