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After all, how does COVID-19 continue to spread? - A simulation (How it was built)

This "project" started with my curiosity about the covid-19 spread around the world. In my own country, and after everyone knows all health authorities' rules, the virus is now attacking harder than never before. Why the virus still continues to spread? This is one of my big questions. So I did this simulation-like project to study the influences of people behaviours in the overall number of infected people. As an example, evaluating the influence of 50% of the people within an area not wearing a mask, or not respecting the social distance as we should.
Stop The Spread. Image created by Hazem Asif. Submitted for United Nations Global Call Out To Creatives 

To build the simulation I used the python programming language and the Object-oriented programming paradigm. I did not use any python library to build the simulation environment itself. It is just two classes to represent the Agents and the Simulation.

Classes

  • Simulation
    • The class that stores the list of agents in the simulation and "performs tasks" such as updating the health status, the quarantine zone, and move the agents on each day of simulation.
  • Agent
    • The class representing the agent where we defined each agent's characteristics such as age, position in the environment, health status, name, number of days with the virus,  and so on.

The simulation is based on random agent behaviours to simulate the free will of real persons. So, the functions to move the agents use a randomly generated number in the x-axis and y-axis. I also added a parameter to limit the agent's movement ray to test cases where the agents' moves are limited, and a social distancing value that forces the agents to choose a position, when they need to move, with a distance to another agent greater than the social distance value. 

As you must already found out, the simulation has two dimensions to represent the agent's position (x-axis and y-axis). The environment's size is also configurable to evaluate the free space impact in the virus spread.

With regard to the agents, we have two different ways of creating them: 

  1. We can create n agents based on probabilities of being Healthy, Sick, Asymptomatic, or the agent's immune system response to the virus being moderately infected, etc. 
  2. The other possible way is by specifying the number of agents of each type of health status and immune system response. 
Please note that only in the first case the age of each agent is used as the factor to indicate the immune system response. The second approach, defined as static begging in the simulation, do not take into account the age of the agent. Here, the immune system response type is randomly distributed by the agents, according to the specified numbers for each type.

The age influence

In the random beginning approach, I use probabilities based on real data from my country when it comes to death data by age range. The data was retrieved from the response of one service from this site. After that, I stored the data to have it in a file (I chose not to call the service and avoid that implementation since it takes time). Then I handle the data so I can have, for instance, the percentage of people that die grouped by age range. You can find that logic at the end of constants.py file. In other cases, I simply defined probability values that I think are valid. Almost all values can be changed in the configuration file (config.yaml) if you want to test the Simulation. The file also includes the variables' meaning in the simulation context through code comments. 

Using the Simulation

After having your own configurations, you can run the simulation in a trivial graphic mode. This simulation mode shows an image in a black background and the agents represented by pixels with different colours, also configurable in the config.yaml file. In each simulation day, the image is updated. I chose this approach as it is quite simple and quick to develop, and my focus was not in the graphical part. You can also run a parallel small script to show you, in real-time, the current simulation's cumulative values. To run the simulation you should read the README.md file at the root of the project.
Simulation graphical mode

Simulation Test Cases and Outputs

For readability, I had split the post into 3 so you can find the key points you want to know. The simulation results can be found here.

Code

You can find the simulation's code at Github.

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