This interactive simulator demonstrates how infectious diseases spread through populations using a deterministic, mean-field SEIRS compartmental model. It features real-time parameter adjustments and elegant visualizations to help understand epidemic dynamics, including vaccination, immunity waning, and disease-induced mortality.

• A modern web browser (Chrome, Firefox, Safari, or Edge)
• No installation or build process required!

1. Get the code

   git clone https://github.com/denniemok/seirs-demo.git

   Or download ZIP directly.

2. Open in browser

   Double-click index.html to open directly in your browser, or use a local web server for full functionality:

   cd seirs-demo
   
   # Python 3
   python -m http.server 8000
   
   # Node.js
   npx http-server

   Then open http://localhost:8000 in your browser.

The model divides the population into four compartments:

The dynamics are governed by these differential equations:

dS/dt = -βSI + ωR - μS + μ(1-p)
dE/dt = βSI - σE - μE
dI/dt = σE - γI - (μ+α)I
dR/dt = γI - ωR - μR + μp

seirs-demo/
├── 📄 index.html          # Main HTML page with structure and equations
├── 📜 seirs.js            # Core SEIRS model implementation and plotting logic
├── ⚙️  params.js           # Parameter definitions and UI control values
├── 🎨 seirs.css           # Styling and responsive design
└── 📊 d3.min.js           # D3.js library for visualization

Edit params.js to change parameter ranges and defaults:

// Example: Extend R₀ range from 1-5 to 1-10
R0: generateParams(1, 10, 0.1, 3.0)

Modify seirs.css to customize the appearance. CSS variables make theming easy:

:root {
    --color-susceptible: #2c3e50;  /* Dark blue */
    --color-exposed: #27ae60;      /* Green */
    --color-infectious: #e67e22;   /* Orange */
    --color-recovered: #3498db;    /* Light blue */
}

This project is built upon and inspired by:

• 📚 seir-demo by Rob Moss
• 📚 posepi2 by Martin Krzywinski
• 📊 D3.js by Mike Bostock
• 📖 Bjørnstad, O., Shea, K., Krzywinski, M. & Altman, N. The SEIRS model for infectious disease dynamics. Nature Methods 17:557–558 (2020)

This model is intended for educational purposes to understand epidemiological dynamics. For public health decisions, please consult epidemiological experts and use validated, peer-reviewed models.

This project, including D3.js v3.5.17, is distributed under the BSD 3-Clause License.