Simulation rules are almost the same to what I coded using Python but with one interesting addition a partial

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Simulation rules are almost the same to what I coded using Python but with one interesting addition – a partial immunity (see details below). The main difference between the Python version and R-version of the code would be the use of vectorization. You should use as little as possible of loops. In fact, there should be just one loop for the days-count. If anyone tries to create the exact copy of my Python code but in R, the grade will be quite low. Below are rules that should help you building a simulation model:

1. We assume that there are “N_population” citizens in a population. “N_population” is an input parameter. During development and testing you can have it small, but your code should be able to handle a reasonably large value for population size.2. Every citizen has a health state – “healthy, sick, dead” (or you can code it as 0, 1, 2). When we start, all citizens are alive and healthy3. To start the pandemic, you randomly mark a small number of citizens as “sick” – there can be a parameter for the number of initially infected citizens. You can start with 1 or 2 initial sick cases.4. One iteration is one day. During the day, every citizen can meet a random number (say between 0 and 20 inclusive) of randomly selected citizens. You don’t really need to control all citizens as we are interested in meetings of sick people only. We don’t care how many healthy people meet each other.5. Every sick citizen can stay sick and infectious for 10 days, hence you should have some counter for each sick citizen. After 10 days a sick citizen becomes healthy and stops spreading the virus6. Obviously, dead citizens cannot become sick, they don’t meet anyone and, as a result, cannot infect anyone.7. During the day every sick citizen has a probability to die (mortality rate) from the disease with probability 0.5% (quite low probability).8. If a sick citizen does not die, then they can meet other people as per the rule 4 above, and if they meet a healthy person, that person might become sick too and start infecting other people starting from the next day (that is an infection day is day 0 of their sickness). The probability for a citizen of becoming sick (infection rate) after a contact is 30% (this is quite high).9. (New rule!) After surviving the infection and getting healthy, the person becomes immune. This is a partial immunity. It does not make the person “invincible” but reduces the chance of infection in future meetings with sick people. Take the immunity coefficient as 0.1. That is, a probability of being infected for immune person is ten times lower than for the person without an immunity. The infection rate for the immune person is the “original” infection rate multiplied by the immunity coefficient, e.g. (0.3*0.1). For the test, you can set immunity coefficient equal 1, which means no benefits of immunity and the result should be the same as in Python example I presented.10. You should run this simulation for a number of days (iterations) and store each day results in a data frame: how many sick citizens in population in total, how many people died, how many new infections per day, something else you might find interesting or useful (e.g. R0). After completing the simulation, you should create a data visualisation of the history of infection.11. You can run simulations for a predefined number of days, say 100 or 300 days, or till some natural outcome – all citizens get healthy, or all citizens die12. Keep all parameters in the beginning of the code, so you can easily change them without a need to change anything in the code.13. Try to change parameters of the model – increase mortality rate (more dangerous virus scenario – SARS and MERC had 3% mortality); or decrease infection rate (say, 5% – wearing masks and social distance scenario); or increase the length of sickness period; or reduce the number of possible contacts for all citizens to a range between 0 and 2 (lockdown scenario). Make a brief comment (3-4 lines as comments at the end of the Rscript) on what parameters have the strongest effect on the infection growth.The main purpose of this exercise is to test your understanding of programming in R: using vectorization, indexing, plottingHint on getting a random event for a given probability:If you know that some event has probability 75% (e.g., coin tossing on head – this is clearly a biased coin), then you generate a random number from a uniform distribution between 0 and 1, e.g., x <- runif(n = 1)And then, if x < 0.75 is TRUE we assume coin to be on head; if FALSE we assume the coin is on tail.
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