Epidemic intervention simulator

A simplified interactive epidemic simulation tool that demonstrates the impact of treatment timing and effectiveness on infectious disease outbreaks.

Overview

This Shiny web application simulates the dynamics of infectious disease epidemics using a modified SE2IR (Susceptible-Exposed-Infectious[untreated]-Infectious[treated]-Recovered) compartmental model. The simulator allows users to explore how different treatment strategies affect epidemic outcomes across different populations.

Key Features:

• Dual model comparison: Simultaneously compare epidemic trajectories between two populations with different parameters
• Real-time visualization: See how changes in treatment timing and effectiveness alter epidemic curves
• Treatment intervention analysis: Model the impact of antivirals or other treatments that reduce transmission
• Comprehensive statistics: Track peak infections, total cases, and final epidemic state
• Multiple visualization options: View new infections or all compartment populations over time
• Pre-configured scenarios: Explore optimistic, pessimistic, and no-treatment scenarios

The Model

The application implements a deterministic compartmental model with five states:

• S (Susceptible): Healthy individuals at risk of infection
• E (Exposed): Infected but not yet infectious (latent period)
• I₁ (Infectious, Untreated): Infectious individuals who have not yet received treatment
• I₂ (Infectious, Treated): Infectious individuals who are receiving treatment with reduced transmission
• R (Removed): Recovered or deceased individuals (no longer infectious)

The model incorporates treatment timing (average delay to treatment) and effectiveness (reduction in transmission) to simulate realistic intervention scenarios.

Interactive Features

Users can modify parameters including:

• Treatment effectiveness: Reduction in transmission for treated individuals (0-100%)
• Treatment delay: Average time between becoming infectious and starting treatment (0-8 days)
• R₀: Basic reproduction number (average secondary cases per infectious individual)
• Population size: Total population for each model scenario
• Disease progression: Latency and infectious period durations
• Visualization options: Log scale, population normalization, and more

Getting Started

Option 1: Run with Docker (Recommended!)

The easiest way to run this application is using Docker, which ensures all dependencies are correctly installed.

Prerequisites

• Docker Desktop installed on your computer

Quick Start

1. Pull and run the Docker image:

   docker pull jcoa05/influenza-treatment-sim:latest
   docker run --rm -p 3838:3838 jcoa05/influenza-treatment-sim:latest

2. Open your browser and navigate to:

   http://localhost:3838
   

3. Stop the application by pressing Ctrl+C in the terminal

Build from Source

If you want to build the Docker image yourself:

# Clone this repository
git clone https://github.com/jcoa05/seir-shiny
cd YOUR-REPO-NAME

# Build the Docker image
docker build -t influenza-treatment-sim .

# Run the container
docker run --rm -p 3838:3838 influenza-treatment-sim

Then visit http://localhost:3838 in your browser.

Option 2: Run Locally with R

Prerequisites

• R 4.0.0 or higher
• Required packages: shiny, deSolve, ggplot2, tidyr, dplyr, scales, shinyBS, bslib, httpuv

Installation

# Clone this repository
git clone https://github.com/jcoa05/seir-shiny

# Navigate to the project directory
cd seir-shiny

# Install required packages
install.packages(c("shiny", "deSolve", "ggplot2", "tidyr", "dplyr", 
                   "scales", "shinyBS", "bslib", "httpuv"))

# Run the application
shiny::runApp("app.R")

Option 3: Online Deployment

The app can be deployed to:

• shinyapps.io for free hosting
• Posit Connect for enterprise deployment
• Cloud platforms (AWS, Azure, DigitalOcean) using the included Dockerfile

Mathematical Foundation

The application uses ordinary differential equations (ODEs) to model disease transmission:

dS/dt = -β * S * (I₁ + (1-θ) * I₂) / N
dE/dt = β * S * (I₁ + (1-θ) * I₂) / N - ω * E
dI₁/dt = ω * E - φ * I₁ - γ * I₁
dI₂/dt = φ * I₁ - γ * I₂
dR/dt = γ * I₁ + γ * I₂

Where:

• β = transmission rate
• ω = rate of progression from exposed to infectious (1/latency period)
• γ = recovery rate (1/infectious period)
• θ = treatment effectiveness (reduction in transmission)
• φ = rate of treatment initiation (1/treatment delay)
• N = total population

Docker details

The Docker container is based on the Rocker Shiny image and includes:

• R 4.3.2
• Shiny Server
• All required R packages pre-installed

Future Development

Planned enhancements include:

• Stochastic modeling options
• Age-structured populations
• Vaccination interventions
• Hospitalization and healthcare capacity modeling
• Exportable reports and data analysis
• Regional comparison tools

Contributing

Contributions are welcome! Please feel free to submit a pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License.

Acknowledgments

• Based on epidemiological models developed for public health response planning
• Special thanks to the R and Shiny communities for their excellent documentation and support
• Docker containerization enables reproducible research and easy deployment

This simulator is intended for educational purposes. Real-world epidemics involve complex social, biological, and environmental factors.