---
title: "Introduction to 'EpiSimR'"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to 'EpiSimR'}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)
```

# Overview

'EpiSimR' is an R package providing an interactive **Shiny** application for simulating the spread of **epidemic and endemic diseases** using **deterministic compartmental mathematical models**. The application allows users to:

-   Select different epidemiological models (**SIR, SEIR**).
-   Consider key factors such as **immunity, demographic changes, vaccination, and isolation strategies**.
-   Adjust model parameters dynamically (e.g., **basic reproduction number R₀, infectious period, vaccination coverage**).
-   Visualize the impact of interventions through **real-time interactive plots**.

This tool is designed for **researchers, public health professionals, and students** who wish to explore the dynamics of infectious diseases and assess intervention strategies.

# Installation

To install and load 'EpiSimR', use:

```{r}
# Install from CRAN
install.packages("EpiSimR")

# Load the package
library(EpiSimR)
```

# Launching the Application

To start the **interactive Shiny app**, run:

```{r}
run_app()
```

# Features

## 1. Model Selection & Customization

-   **SIR vs. SEIR models**: Choose between the classic **Susceptible-Infected-Recovered (SIR)** or **Susceptible-Exposed-Infected-Recovered (SEIR)** model.
-   **Immunity options**: Decide whether recovered individuals gain **permanent or temporary immunity**.
-   **Demographic changes**: Option to include **birth and mortality rates** in the model.
-   **Public health interventions**: Assess the impact of **vaccination and isolation strategies**.

## 2. Adjustable Parameters

-   **Basic reproduction number (R₀)**.
-   **Birth and mortality rates**.
-   **Infectious period**.
-   **Latent period** (for SEIR models).
-   **Duration of immunity**.
-   **Vaccination coverage**.
-   **Isolation rate**.

## 3. Simulation & Visualization

-   **Real-time simulation**: Run simulations dynamically as parameters are adjusted.
-   **Graphical visualization**: Generate plots showing disease dynamics over time.
-   **Comparative analysis**: Assess the effectiveness of different control measures.

## 4. User-Friendly Interface

-   **Interactive UI** built with the **Shiny** package.
-   **Dynamic updates** based on user input.
-   **Export options** for simulation results.

# Example Use Case

Imagine a scenario where a new infectious disease emerges. Public health officials want to evaluate whether **vaccination or isolation measures** can help control the outbreak. Using **EpiSimR**, they can:

1.  Select an **SEIR model** to account for an incubation period.
2.  Set an initial **R₀** of 3.0 (high transmission potential).
3.  Introduce a **vaccination strategy** covering 60% of the population.
4.  Observe the resulting **reduction in peak infection levels**.

# References

For more details on deterministic compartmental models, see:

-   **Brauer, F. (2008).** *Compartmental Models in Epidemiology*. In: F. Brauer, P. van den Driessche, & J. Wu (Eds.), *Mathematical Epidemiology*. Springer. <doi:10.1007/978-3-540-78911-6_2>.
-   **Keeling, M. J., & Rohani, P. (2008).** *Modeling Infectious Diseases in Humans and Animals*. Princeton University Press.

------------------------------------------------------------------------

### Citation

If you use 'EpiSimR' in your research, please cite it as follows:

``` r
citation("EpiSimR")
```

------------------------------------------------------------------------

This vignette provides an introduction to using 'EpiSimR' for epidemic simulations. For further details, refer to the package documentation and function help pages (e.g., `?run_app`).
