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false1.0 What would be a sensible hypothesis on how population size affects the elapsed time?
5.2.2.1: The most obvious hypothesis is that a larger population takes longer for everyone to get sick, supposing the rate of infection doesn't change. But in fact the rate of infection /does/ change for a larger population in the same area, which is why low-density businesses such as hairdressers could reopen sooner than high-density ones such as churches.2.0 What if the people interacted with one and other more frequently?
5.2.2.2. If the space gets bigger without increasing the population, the infection rate will probably go down.3.0 Click here to load a starter project. Save to your Snap! account.
It includes:
6 Global variables
population
percent initially infected
speed
sprites ready?
number infected
infected?
Two costumes
sick
well3.0 Click here to load a starter project. Save to your Snap! account.
It includes:
simulate disease function for you to complete
initialize simulation block
touching infected person? predicate for you to complete
initialize clone block
when I start as a clone script4.0 Complete the
4.a simulate disease function, 4.0 Complete the
4.b the touching infected person? predicate, and 4.0 Complete the
4.c the when I start as clone script.infected?truesickThere are many ways to program this, of course, and our solution isn't necessarily the most elegant possible. Bugs in our first version:
The clones don't automatically disappear when the block reports the elapsed time. If this were a command instead of a reporter, it could just end with a STOP ALL, which would delete all the clones at once. Instead a WARP is required to avoid seeing them disappear one by one.
The first version of this block got faster as more clones were infected, because only healthy clones have to detect having touched an infected one. To fix that, this version redundantly checks neighbors for infection even for clones that are already infected.
It took a while to figure out that in this visual simulation, the effective population density (which is what determines the speed at which the entire population becomes infected) depends on the size of the sprite's costume! This version sets the sprite size to 50%, which gives a much more reasonable spread of times than full-size sprites.
This isn't a bug, but note the use of MY NEIGHBORS, so that only nearby clones are tested for touching.
Also note that INFECTED? is an instance (for this sprite only) variable, so that each clone has its own version. 5.0 Discuss how your simulation is similar to and different from a real-life disease outbreak. Identify the simplifying assumptions you made in this simulation.
5.2.2.5: The most obviously unrealistic assumption is that an infected person remains infected forever, neither dying nor getting well. This makes our simulation useless in a real situation; one of the big unanswered questions as we write this is the death rate among people who get infected. Most likely there are many people who get infected but have no symptoms or easily managed (mild) symptoms and so don't show up at the hospital for treatment. As a result, we know only the rate at which people with serious symptoms die. Another very important simplifying assumption is that everyone who comes in contact with an infected person is infected. In reality, not only is there a less-than-100% rate, but the rate differs in different populations; old people, for example, are more at risk. More answers are possible.6.0 Write a function that takes population size as input, runs the simulation ten times, keeps the percent initially infected at 10 and the speed of infection at 2, and reports the average time elapsed.Note the technique of mapping over the numbers from 1 to 10, without actually using the selected number, to call the simulator ten times. It'll make us very happy if your students can write this HOF-based solution readily by this point in the course, although it's also fine if they remember that they have an AVERAGE block in their math block library.5.2.2.7: Students will get different numbers depending on the speed of their computers. We get 10.76, 4.78, 2.94, and 1.60 seconds. The effect of population density on the rate of infection turns out to be more important than the fact that more people means more infections needed to include everyone. It's tempting to say that time ≈ 450/size, but if you multiply it out, the speedup for larger populations is a bit greater than that formula would imply.5.2.2.8: The best way to investigate such questions is to reprogram the simulation and see how varying various parameters affects the result. If only some encounters lead to infection, does everyone still eventually get sick? What if people recover after five cycles? What if some fraction of the population is naturally immune? And so on.7.0 Call this function four times with different populations (50, 100, 150, and 200), and write down the results. Create a refined hypothesis about the relationship between population size and time for the entire population to be infected.
50:
100:
150:
200:
Hypothesis:
8.0 In real disease scenarios, not everyone ends up getting sick. How would you decide if the simplifications you used in a simulation invalidate the conclusions you might draw?TIF G: Customer satisfaction depends on many factors, not just average speed. Grocery stores have /categories/ of checkout lines: express lanes, automated self-checkout lanes, and so on. Also, it turns out that the ability to switch lanes if someone in front of you turns out to be really slow gives customers some feeling of control over their wait time (whether or not it's really effective). And the fact that each customer is wheeling a long cart in front of them would make a single line /look/ very long, even if the number of customers isn't so large.
At the bank, it's hard to predict which customers will turn out to have time-consuming problems. (But the bank does have the equivalent of self-checkout lanes, ATMs, just not right next to the human tellers. And they do have a few special lines, for business customers, opening an account, etc.)Updated by Firstname Lastname on 08/17/2021
2122 U5L2p2 Disease Spread Project (solution file)
Unit 5 Lab 2: Simulations, Page 2 > Disease Spread Projectstart timesprites ready?trueelapsed time1000vanishfalse950102true