Agent based model that simulates the virus propagation in a community. The model has the following assumptions:
1- Humans live in a world that has a length and width.
2- Humans can only be susceptibles, infected, or removed.
3- Each human occupy a space of coordinates (x,y) in the world.
4- People who get recover won't be susceptible again.
6- People can move randomly in 8 directions: East, West, North, South, NorthEast, NorthWest, SouthEast, and SouthWest.
7- Susceptibles can only be infected if they existed in a coordinate of (x,y) that an infected person existed in at the same time.
8- If a susceptible person existed at the same position as an infected person, there is a probability of getting infected according to the infection rate of the virus.
9- People who get the vaccine change from susceptibles to recovered.
The model is producing both visual representation of the three populations and a map. This map is for representing the model's world (simulated area). The map has four colors:
Black: means no humans are in this place
Red: Means there is infected person in this place
Yellow: Means there is susceptible person in this place and there is no infected.
Green: Means there is only recovered persons in this place
The model also produce a 3D phase Space that plots the susceptible, Infected, and Recovered population with regard to each others.
- random
- seaborn
- matplotlib
- copy
- IPython
- numpy
- mpl_toolkits
- imageio
- os
1- Clone the repository.
2- Make sure that you have all of the used libraries.
3- Run the Model.ipynp on jupyter notebook.