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interventions.Rmd
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interventions.Rmd
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---
title: "Implementing Interventions with epiworldRShiny"
author:
- Derek Meyer
- George Vega Yon
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Implementing Interventions with epiworldRShiny}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>", out.width = "80%", fig.width = 7, fig.height = 5,
fig.align = "center"
)
```
# Example 2: Implementing vaccine and school closure
## Running the application
To run the epiworldRShiny application, first ensure that the package is
installed and loaded using the following code. To launch the application,
use call the function, epiworldRShiny().
```{r running-the-app}
# install.packages("epiworldRShiny")
library(epiworldRShiny)
# epiworldRShiny()
```
## Model set-up
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.
- **Model**: network SEIRD
- **Disease**: COVID-19
- **% of population infected** = 0.1
- **Probability of exposure** = 0.05
- **Recovery probability** = 0.14
- **Incubation days** = 7
- **Simulation time** = 100
- **Vaccine prevalence** = 70%
- **School closure prevalance** = 50%
- **Day of school closure implementation** = 7
- **Seed** = 2023
## Running the model
```{r, echo=FALSE}
knitr::include_graphics("https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/d5405162-f7fe-4a42-8a4c-e9a2ac31be73")
```
The above graphic demonstrates launching the application and running the model
with COVID-19 as the disease. To modify the intervention parameters, scroll to
the bottom of the application sidebar, select "interventions", and modify as
desired. After running the simulation, plots of the distributions of states and
the disease's reproductive number over time, a model summary, and table of each
state's counts over time are all displayed.
## Results
In this example, the model of choice is a SEIRD Network model. Notice the day of
peak infections occurs on day 12, maxing out at 3,882 infections. Notice in
the SEIRD model plot, there are very few exposed, infected, and deceased agents
while the number of susceptible and recovered agents over the course of the
simulation changes rapidly. Due to the vaccine, which decreases the probability
of infection, and school closures which decrease the probability of exposure to
COVID-19, there are a significantly decreased number of exposed, infected, and
deceased agents.
## Comparison to absence of interventions
```{r, echo=FALSE, fig.show='hold',fig.align='center'}
# library(cowplot)
# library(magick)
# ggdraw() +
# draw_image("~/Desktop/intervention.png", width = 0.5) +
# draw_image("~/Desktop/comparison_plot.png", width = 0.5, x = .5)
knitr::include_graphics("https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/092caa29-dee2-4230-b2c7-59a46a2493ee")
```
The above SEIRD model figures demonstrate the distribution of states over time
both with and without interventions present (left and right figures
respectively). With no measure to combat the spread of COVID-19, the number of
exposed, infected, and deceased individuals greatly increase compared to the
model with interventions. The peak number of infections occurs earlier, on day
11, with a total of 17,616 infections at the peak, compared to 3,882 infections
on day 12 with interventions present. This indicates that the vaccination and
school closing measures were effective in reducing the number of infections and
deaths in this simulated population.