Introduction to 'EpiSimR

In EpiSimR: A 'Shiny' App to Simulate the Dynamics of Epidemic and Endemic Diseases Spread

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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. .
• Keeling, M. J., & Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. Princeton University Press.

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Citation

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

citation("EpiSimR")

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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).

EpiSimR documentation built on April 4, 2025, 1:07 a.m.