Model the Spread of COVID-19

Cellular automata can be used for numerous applications beyond Conway's Game of Life. By applying different rules, cellular automata can model a variety of complex systems, such as population simulations, disease spread modeling, forest fire spread, and traffic flow, which are used in fields like ecology, epidemiology, and urban planning.

For simulating the spread of COVID-19, we can assign different states to cells: susceptible (white), infected (red), and recovered (green). The rules to implement are:

1. A susceptible cell (white) with one or more infected neighbors (red) becomes infected (red).
2. An infected cell (red) remains infected for a fixed number of generations and then becomes recovered (green).
3. A recovered cell (green) remains recovered and does not become susceptible again.

A combination of base probabilities derived from real data and the current state of a cell's neighborhood can be used to make these steps reflect the real situation more accurately. (These will be parameters in your systems whose values you can change as desired).

Each generation corresponds to a time step in the simulation. Different initial conditions can correspond to various real-world scenarios, such as a few initial infections in a small community or widespread infections in a densely populated area.

Printing statistics with the simulation provides valuable insights:

• Population Growth: Indicates the rate at which infections are spreading. A high growth rate suggests rapid disease transmission.
• Stability: Reflects how the spread is stabilizing over time. High stability indicates that the infection pattern is becoming consistent.
• Entropy: Measures the randomness or disorder in the spread of the infection. High entropy suggests a chaotic and unpredictable spread.
• Equilibrium: Shows whether the infection spread has reached a steady state or continues to fluctuate. Reaching equilibrium means the infection levels are stabilizing, either spreading uniformly or not changing significantly.

So your task here is to write code to implement this simulation and experiment with different start states to observe how the infection spreads under various conditions.