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How does COVID-19 continue to spread? - A simulation 2.0 (Results)

This post shows some of the results we can find by using the simulation. As in the first version I made some tests, now I focused the new tests on the travelling and vaccination processes. These two processes were added in the last simulation version and represent some critical behaviour and processes in the virus spread. Photo by Sharon McCutcheon on Unsplash Vaccination process impact Using the standard static configuration values we can find the following results: The vaccination process does not have a considerable impact if we close our borders. By not receiving new agents with the infection, the simulation reaches the number of 0 infected agents on the 38th day using a vaccination percentage of 0.1 If we increase the vaccination percentage to 0.9 the 0 infected agents threshold is reached on the 39th day. Thus, we can infer that if we control the flow of agents in a city/simulation, the vaccination process does not have a considerable impact as it takes some time until the people
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How does COVID-19 continue to spread? - A simulation 2.0 (How it was built)

 Unfortunately, the days we are living right now are still bad, or even worse than ever. Millions of people are being killed by this "new virus", as they called it once. COVID-19 is here and will be among us for too long. Some of us thought, incorrectly, 2021 will be the year, we will have vaccines, that's it! No more problems related to COVID-19! Let's start living as before!  No, no, no! If you still think this way, please stop it right now. By not respecting the known procedures to avoid the COVID-19 infection you will keep the virus spreading chain. Consequently, the virus will kill more people, being them related to you or not. Many apparently  healthy humans are having severe "side effects" by getting infected with this virus. Stop thinking the virus provokes just flu and help to stop the spread!  Millions of healthcare professionals are giving their lives to help in this war. You are neglecting them and all the people around you! Keep yourself safe
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After all, how does COVID-19 continue to spread? - A simulation (Conclusions)

This post is the final chapter of my experiments with the simulation. Here I will point out the conclusions we can get from running each test.  Again, please note that the simulation does not completely simulate the human and virus behaviour and it is based on known assumptions and probabilities. Therefore, some tests can have a different outcome from real life. However, we can infer some risky behaviours to avoid in our daily life. Image created by Rawpixel Images. Submitted for United Nations Global Call Out To Creatives Every health authorities states we should wear a mask, wash our hands or use alcohol gel, avoid crowded places, to be 2 meters apart from other people. Well, after running several tests in my simulation I can say they are completely right! If you do not trust them for some reason, please look to the results I got ( here ).  We are now starting another spread. The vaccine against COVID-19 spread. I really hope it does not have any serious side effects. I believe i
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After all, how does COVID-19 continue to spread? - A simulation (Results)

Here I will present some tests and analyze them to find out some COVID-19's behaviours.  Again, please note the simulation is a simple model where the outputs can be different from the ones in the real world.  Image created by Underway Studio. Submitted for United Nations Global Call Out To Creatives Tablets| Computers, Tablets & Components |Desktops| Laptop Accessories| Laptops I suggest you use the simulation yourself and tweak the configurations file to better understand the simulation's behaviour.  By using the config.yaml file to change some of the simulation parameters we can test different case scenarios to better understand the spread of the virus.  Sometimes we mislead ourselves by taking someone's statement as the absolute truth and that leads us to some mistakes. And you know, against COVID-19 any mistake counts and can get you and your loved ones in real danger. So I will show some tests results and analyse them in this post
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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
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Reinforcement Learning (Part III) - Exploration vs Exploitation

In the Reinforcement Learning field, we face ourselves with the exploration and exploitation words.  Moreover, many articles talk about the exploration vs exploitation trade-off.  What do they mean? Why is this a thing in RL?  Does this relationship have a big impact on the RL algorithms' outcome? Figure 1 - Should I choose the well-known path or give a try to a new one? Photo by Jens Lelie on Unsplash Exploration Exploration is when the agent explores new steps and/or actions to find if other state-action pairs yield a better reward from the environment. You can explore the whole world, or a sample of it to find out the rewards you can get.   Imagine the case where you need to lunch somewhere in your city. You have two options, in the first one you go to the same restaurant you always go with that tasty food you like. The other option is choosing a different restaurant and only after being there you find out if the food is better, equal or worst.   The second option leads you
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Reinforcement Learning (Part II) - Model-free

Today's post will introduce you to the model-free methods of Reinforcement Learning (RL). To have a model of the environment we need to store all the states and actions. To do so, we are limited to the infrastructure limits, that means we need to find another approach when the environment is too big and with too many variables. Therefore, model-free approaches can handle the problems where the world is too big to fit our infrastructures. To better comprehension, the Q-Learning algorithm will be presented and explained. Robot infinite environment - Photo by Dominik Scythe on Unsplash Terminologies Figure 1 - Agent-environment interaction Agent  — The learner and the one that makes actions. The agent's goal is to maximise the cumulative reward across a set of actions and states. Action  — A set of actions which the agent can perform. Different environments allow the agent to perform distinct kinds of actions. The set of all valid actions in a given environment is usually denomi
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