# Introduction to ‘EpiSimR’

# 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:

    # Install from CRAN
    install.packages("EpiSimR")

    # Load the package
    library(EpiSimR)

# Launching the Application

To start the interactive Shiny app, run:

    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:

    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`).