Computational Physics – PHYS3071/7073 School of Mathematics and Physics
University of Queensland
Instructor: Pat Scott
Due: August 31, 4pm
Assignment 4 — The Superspreader
Question 1.
You are an infectious diseases physician working at the Princess Vanellope Hospital for the
Digitally Challenged. You are a committed doctor / generous soul / like to live dangerously /
all of the above, so you’re currently seeing one (different) COVID positive patient each week.
You rock the PPE like nobody else, but there is still at least a 1% chance that you will catch
COVID from any given positive patient that you see. If that person is younger than 40, the
probability goes up to 2%; if they are younger than 30, it goes up to 3%. If they are older
than 70, it also goes up to 3%.
You also take weekly night classes in quantum cryptozoology organised by the folks from the
local node of TWEET (the ARC Centre of Excellence for Terrifically Weird but Enticingly Eco-
nomical Technologies). You sit so far away from the other students that you’re not completely
convinced that they even exist. However, you often go and ask the lecturer fun questions
like “Is the US Government suppressing the real truth about Bigfoot?” in person after class.
Despite the lecturer’s claims to need to “hurry up and catch a train” most weeks, there’s about
a one in 10 chance per lecture that if you have COVID, you will pass it on to the lecturer.
Princess Vanellope Hospital’s catchment area is sufficiently large that the age distribution
of its patients is pretty representative of Australia as a whole. Consider the age data in
age.csv, on the age distribution of confirmed COVID cases up to Aug 7 in Australia, to be a
reasonable bootstrap estimate of the overall probability distribution for the ages of COVID
sufferers in Australia (and therefore at Princess Vanellope Hospital). These data come from
http://www.covid19data.com.au/demographics (and are the only thing in this assignment not
completely invented).
By Monte Carloing the possible outcomes, work out the probability in a 3-month period
that you will get COVID and pass it on to your night class lecturer. Make sure to either
demonstrate, or better yet, guarantee within the structure of your code, that your estimate
is properly numerically converged. Make a plot showing the counts of your samples from
the distribution of ages, demonstrating that your samples do indeed reproduce the target
distribution.
Computational Physics Assignment 4 Semester 2 20211
Question 2.
You’re not really very happy with the level of risk that this implies for your fantastically
inspiring lecturer. What would you do if you couldn’t learn about quantum cryptozoology
from such an authority? To mitigate this, would you be better off
a) Refusing to see any COVID patients under 30?
b) Refusing to see any COVID patients under 40?
c) Refusing to see any COVID patients over 70?
d) Just attending every second night class via Zoom?
Again, make plots showing that the counts of your samples from the distributions of ages do
indeed reproduce the target distributions.
Health warning: This assignment is a work of fiction. Any resemblance to real numbers living
or dead is purely coincidental. May you suffer the wrath of Boris the hamster if you treat them as
otherwise.
Grading
A : /50% Code: Does the program run and produce the correct output?
B : /15% Usability: Is the program easy to use? Are the input requirements and output
formatting easy to understand?
C : /15% Readability: Is the program easy to read and comprehend? Is it well-commented?
If the code is sufficiently complex, has it been broken up into manageable subroutines, each of
which is well-documented?
D : /10% Accuracy: are you able to identify the action that will best protect your lecturer?
E : /10% Presentation (Plots): Do(es) the plot(s) clearly convey the results? Are the axes
and the plot items clearly labeled? Was the correct style (points or lines) used for each item?
Total: /100%
Prepared by Pat Scott on August 10, 2021.
Computational Physics Assignment 4 Semester 2 20212