Assignments WS 2020/2021

Spread of 19-COVID

Author : Toscool (talk) 11:32, 11 December 2020 (CET)

Simulation : I will build a dynamic model of an epidemic his the dynamic issues. I will focus on the spread of Covid-19 in a population and Specially I will try to modelize the effect of a lockdown on the number of infections and deaths. I will start with a susceptible population, with one infected people that infect 10 people with a certain probability. When you are infected you go the doctors and with a certain probability you go in the recovery population or in the hospital. Then you have a percentage of chance to die in this hospital or to recover. The lockdown will triggered when the ratio of people who where to the doctor and total population exceed a certain percentage. We lockdown and by reducing the number of people you infect in the beginning.

Goal of simulation : I aim to model the impact of a lockdown on the spread of COVID on a population. It will also show the difference between countries, for example if we lockdown too late. What will be the impact of these decisions.

Method : I will use VensimPLE for this simulation, as it is a dynamic system, and In my home university I know that I will need to use VensimPLE for a future project and course so it will help me a lot.

The topic souds good - the question is, on what data sources will you base your simulation (equations) on? Oleg.Svatos (talk) 22:13, 12 December 2020 (CET)

I will use the case of Switzerland and use data from this website (https://corona-data.ch). I will try to reproduce the number of cases and the speed of the spread.Toscool (talk) 13:39, 13 December 2020 (CET)

NFL Free Agency

Author : TimWalenczak (talk) 15:02, 11 December 2020 (CET)

Simulation : Free agency is a period of time during the off-season in the National Football League in the US (few weeks). During that period, all 32 teams in the league can sign active players that have no contract with any other team, so called free agents. The process goes as follows: at the beginning of free agency a certain amount of players become free agents due to expiring contracts. All players can play one certain position and want to receive a certain salary. While all the teams are looking for players to play a particular position and have a certain amount of money they can spend on players. Demand and supply of a specific position group influences their value. A certain supply-and-demand-factor for each position group adjusts a player's desired salary according to his position group. Players and teams get in touch when they are located next to each other. When the position needs matches the offer and a player’s salary fits the money a team offers, that player is signed by the team and disappears from free agency market. If the offer doesn’t fit the need, a player walks on to the next team. With every round/tick/signing the supply-and-demand-factor and thus the value of players in each position group change and so does the amount of money teams are able to spend on further players. Free agency ends after a distinct number of ticks or even earlier if all players are signed or teams don’t have any money left to spend.

Goal of simulation : is to find out how a player's value changes during the period of free agency. Especially interesting is, whether players that sign a new contract early in the period received higher payments or the ones signing late in the period. Maybe a certain strategy can be deducted for players depending on their initial situation (supply-and-demand-factor) in order to reach a highly-paid contract.

Method : Since I don’t know VensimPLE yet, I think I will use NetLogo, because I see some similarities between my case and the “Escape building” case we modelled recently.

It is a bit unusual, but I can see something in common rather with Market Structure than with Building Escape. Please, just elaborate it into a greater detail and specify, how exactly should your simulation work. I am not sure you have a coherent idea about the solution. But if you work this out, I don't see a problem. Tomáš (talk) 21:45, 11 December 2020 (CET)

You can’t outrun your fork!

Author : Michal F. (talk) 11:23, 13 December 2020 (CET)

Simulation : It is known that being fit is 80% diet and 20% exercise. I would like to simulate the time needed to reach the Ideal Body Weight (IBW) influenced by lifestyle - based on the parameters provided. Calculations will be made on the basis of nutrition-related equations. For example: https://bit.ly/2JZdnl8, https://bit.ly/2KlDClo. Input parameters will be, for example, body weight, height, sex, consumption of individual macronutrients, proactivity of activity and stress, etc. Some random phenomena such as injury or cheat day may be included.

The result of the work should be a simple interface for calculations with the possibility to change the input parameters.

Goal of simulation : Simulate the time needed to reach the Ideal Body Weight (IBW) influenced by lifestyle - based on the parameters provided.

Method : I will use VensimPLE or Insight Maker for my simulation, as it is a dynamic system.

This topic has been attempted before (with some dynamic additions) and it did not end up well, since it is only one way calculation and not a simulation of a dynamic system (it is missing the feedback loops). Please, try something else. Oleg.Svatos (talk) 09:59, 14 December 2020 (CET)

COVID-19 mass testing

Author : Milan P (talk) 20:15, 13 December 2020 (CET)

Simulation : I will create simulation for mass Covid-19 testing in Slovakian village or small city. In previous rounds of this real mass testing in Slovakia testing itself took place in one day and in one sampling point, this will be the same with this simulation. In this model roles (medical staff, administrative workers, and police officer), will have assigned costs to them in order to include financial aspect into the simulation. Duration of the simulation will be 4 hours. This will serve as base model which will be adjusted in two ways. Firstly, I will create another model in which I will duplicate sampling point so 2 sampling points will be in simulation. Second adjustment to the base model will be that simulation time will be prolongated to 8 hours. After running all 3 models, outputs will be compared and the optimal one will be selected.

Goal of simulation : In the previous rounds of COVID-19 mass testing in Slovakia certain sampling points had experienced massive overload and were not prepared for the amount of citizens incoming to be tested. Therefore, this simulation would aim to simulate the scenario with data from previous testing rounds and might be helpful if the governments decides to repeat mass testing again.

Method : Discrete simulation - SIMPROCESS

Nanorobots vs. Cancer

Author : Dufa00 20:24, 14 December 2020 (CET)

Simulation : Simply put: I want to simulate human body environment, where cancer is going to spread, but can be cured via nanorobotics. At the begining, observer will adjust parametres that will state the chance of cancer (bad vs. good life style etc.). Then the simulation of cancer will happen. With assigned button, observer can deploy nanorobots (which feed on the cancer) and watch how the robot or robots manage to heal the human body from cancer. If the observer does not press the deploy button, the human body will eventually succumb to the disease. I am still thinking about puting there a little twist, danger that comes with puting nanorobots into human body to see, if the treatment is worth the risk or not.

Goal of simulation : Main goal is to highlight the potential effectiveness of nanorobots. Sidequest might be to compare pros and cons that come along with deployment of the nanorobots.

Method : NetLogo

Widening the spread between rich and poor

Author : Sára 21:00, 14 December 2020 (CET)

Simulation : After 2007-9 world economic crisis a new monetary policy, rather unconventional, was established to escape recessions/depressions. It is called quantitative easing. Although this tool succeeded in reverting the depression and proved helpful during coming years, it is highly discussed what side effects this tools has. A lot of researches suggest that it widens the gap between rich and poor people and make prices of bonds and stock highly overheated.

Goal of simulation : Based on macroeconomics theory and semantic experiences from quantitative easing and its influence on other macroeconomic agents like interest rate, inflation, monetary supply etc., i would like to simulate behavior of banks and people during quantitative easing and i would like to work with different values of macroeconomic agents and bank and people behavior in order to try to find out whether widening the spead between rich and poor is an unevitable effect, whether it is sustainable in long term and whether they are possibilities how to erase this side effect.

Method : NetLogo