Chapter 9. Modeling Infectious Disease with Cellular Automata

One of the goals of data analysis is to understand the system we are studying and modeling is the natural way to understand a real-world phenomenon. A model is always a simplified version of the real thing. However, through modeling and simulation we can try scenarios that are hard to reproduce, or are expensive, or dangerous. We can then perform analysis, define thresholds, and provide the information needed to make decisions. In this chapter, we will model an infectious disease outbreak through cellular automaton simulation implemented in JavaScript using D3.js. Finally, we will contrast the results of the simulation with the classical ordinary differential equations.

In this chapter, we will cover:

• Introduction to epidemiology

  • The epidemiology triangle

• The epidemic models:

  • The SIR model

  • Solving ordinary differential equation for the SIR model with SciPy

  • The SIRS model

• Modeling with cellular automata:

  • Cell, state, grid, and neighborhood...