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https://github.com/wesdoyle/python_epidemic_simulation

A crude simulation of an epidemic in Python with pygame visualization
https://github.com/wesdoyle/python_epidemic_simulation

epidemic-simulations pygame simulation

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A crude simulation of an epidemic in Python with pygame visualization

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README 

        
# Python Epidemic Simulation



## Summary



### This is not a scientifically correct or rigorous simulation. It is used for programming learning purposes only!



This is a crude simulation of an edpidemic using Python and `pygame`.



The simulation consists of a 2-dimensional space in which a configurable number of epidemiological hosts move and transmit a contagious state with variable linear velocity.



Each host exists in a state of `unexposed`, `infected`, or `recovered`.



## Demos



### No Preventative Measures

![No measures](./example_gifs/simul_no_measures.gif)



### Shelter In Place - Partial Adherence

![Shelter in place](./example_gifs/simul_shelter.gif)



### Limit Travel - Partial Adherence

![Limit travel](./example_gifs/simul_limit_travel.gif)



### Vaccinate, Shelter - Partial Adherence, Variable Drip

![Reduce travel](./example_gifs/simul_shelter_vaccine.gif)



## Key simulation concepts:



### Basic Laws



The laws below govern the simulation:



- When initialized, a configured percentage of adherent hosts follow preventative measures.

- Unless limited by preventative measures, hosts initialize traveling in a random direction in a specified range of speed.

- If an `unexposed` and `infected` host come into contact, the `unexposed` host becomes `infected`

- `infected` hosts, while contagious, gradually recover over time

- All hosts survive

- After a configurable period of time, an `infected` host becomes `recovered`

- `recovered` hosts are not contagious



### Preventative Measures



Several preventative measures can be simulated. A configurable percentage of the population adopting preventative measure can be chosen. Given a percentage of `PREVENTATIVE_MEASURE_ADHERENCE`, a random sample of the population is chosen to behave accordingly.



#### Shelter In Place



The `SHELTER_IN_PLACE` preventative measure sets the velocity of adhering hosts to 0 (permanent)



#### Vaccination



The `VACCINATE_POP` preventative measure provides the adhering hosts with a recovery multiplier. The vaccination for any host provides is a random value between 0 and `VACCINATION_DRIP`, which is added to the recovery constant for any host.

`VACCINATION_DRIP` is intended to simulate the idea that of the percentage of units that adhere, each vaccinates at a variable time. Vaccination effect takes place immediately, even if a host is in unexposed state.



#### Limit Travel



The `LIMIT_TRAVEL` preventative measure sets the initial velocity of adhering hosts to 50%



## Running



### Manually run the simulation



Python 3 is required.



- Install dependencies:

    - `pip install -r requirements.txt`



- Customize parameters

    - Edit any of the provided values in `constants.py` to change boundary conditions.

    - the `PreventativeMeasure.SELECTED` array provides the active `PreventativeMeasures`



- Run the simulation

    - `python universe.py`

   

A `Makefile` is provided for convenience.



 ## Credits

 

Thank you to [Vue Minh Khue's example](https://github.com/khuevu/pygames/tree/master/bouncingball), from which I adapted basic 2-dimensional particle interactions for simulating collisions between circular objects in `pygame`.



 ## Improvements

 

 Improvements, additions, and corrections to the simulation are welcome. Please create a pull request if you would like to contribue.