Adding vignettes

Author: derekmeyer37

.DS_Store

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+---
+title: "Getting started with epiworldRShiny"
+author:
+  - Derek Meyer
+  - George Vega Yon
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Getting started 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"
+)
+```
+
+Welcome to epiworldRShiny, a powerful Shiny app designed to enhance the functionality of the epiworldR package for simulating agent-based models. The package offers a user-friendly interface that enables users to simulate infectious diseases using nine different epidemiological models. With epiworldRShiny, incorporating various interventions such as vaccines, masking, and school closures into your simulations can be done with ease. The package also provides intuitive visualization tools to help interpret and analyze simulation results. Whether you're a researcher, healthcare professional, or student, epiworldRShiny simplifies the process of simulating and understanding the dynamics of infectious diseases.
+
+# Example 1: Simulating a SEIR Network Model
+
+## Running the application
+
+In order 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
+  
+After launching the application, notice the sidebar contains many disease and model parameters which can be modified. Altering these parameters will affect the spread of the infectious disease in the simulated population. Name the disease 
+of interest, specify the parameters of choice (listed below), and select "run simulation". 
+  
+- **Model**: Network SEIR   
+- **Disease**: COVID-19    
+- **% of population infected** = 0.1  
+- **Probability of exposure** = 0.05  
+- **Recovery probability** = 0.14  
+- **Incubation days** = 7  
+- **Simulation time** = 100  
+- **Seed** = 2023  
+  
+## Running the model
+```{r, echo=FALSE}
+knitr::include_graphics("https://github.com/UofUEpiBio/epiworldRShiny/assets/105825983/f4e7d313-e3b6-4ebb-9c0a-ca4d53ef9cea")
+```
+  
+The above graphic demonstrates launching the application and running the model with COVID-19 as the disease. 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 SEIR Network model. Notice the day of peak infections occurs on day 12, maxing at about 18,000 infections. After roughly 40 days, the state's curves taper off, meaning that the majority of the population has recovered from the disease. The reproductive number plot demonstrates that the disease spread rapidly at the beginning of the simulation, and drastically decreased over the first 10 days. The model summary returns important information about the simulation such as the model choice, population size, simulation speed, disease(s) present, any tool(s) present, and model parameters. The distribution table displays the counts for each state at baseline and conclusion. The transition probabilities table displays the probability of moving between states. For example, the probability that a susceptible agent remains in the susceptible state is 0.62, with a probability of moving to the exposed state 0.38. Lastly, the counts table shows the state's counts over time, marking the peak infection count in bold font.
+  

vignettes/.DS_Store

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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{Getting started 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("~/Desktop/Research/comparison.png")
+
+```
+
+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.