"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	1	"Set the model parameters to the following: susceptible _S_ = 500, and initially untreated infected host I~u~ = 1. No other infected hosts, no recovered. Set simulation duration to 300 days, start at day 0, time step doesn't matter. Assume that untreated individuals transmit at _b~u~_ = 0.001, treated at _b~t~_ = 0.0005, and resistant at _b~r~_ = 0.0008. Assume that the duration of the infectious period is 5 days for untreated, 4 days for treated and 5 days for resistant (for those individuals, treatment has no effect). Set the rates _g~i~_ accordingly. Assume nobody receives treatment and no resistance is generated (_f = c~u~ = c~t~ = 0_). Set the number of simulations to 20, random seed 123. With parameters set to correspond to the scenario just described, run the simulation. You should see 12 of the simulations with outbreaks and the others without. Only untreated infected will differ from zero. Make sure you understand why. For those simulations with outbreaks, you should have around 50-85 susceptible left at the end."	"T1R1"	"Average number of susceptible at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	2	"Now we'll explore treatment. Set initial untreated infected to 10 (to ensure we get outbreaks). Consecutively set the fraction receiving treatment, _f_, to 0, 0.25, 0.5, 0.75 and 1. For each treatment level, run 20 simulations (be patient) and record the average value of recovered at the end. Based on that, compute the reduction in total total number of infected as treatment level increases."	"T2R1"	"Number of averted infections going from f=0.25 to f=0.75"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	2	"Now we'll explore treatment. Set initial untreated infected to 10 (to ensure we get outbreaks). Consecutively set the fraction receiving treatment, _f_, to 0, 0.25, 0.5, 0.75 and 1. For each treatment level, run 20 simulations (be patient) and record the average value of recovered at the end. Based on that, compute the reduction in total total number of infected as treatment level increases."	"T2R2"	"Number of averted infections going from f=0 to f=0.5"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	2	"Now we'll explore treatment. Set initial untreated infected to 10 (to ensure we get outbreaks). Consecutively set the fraction receiving treatment, _f_, to 0, 0.25, 0.5, 0.75 and 1. For each treatment level, run 20 simulations (be patient) and record the average value of recovered at the end. Based on that, compute the reduction in total total number of infected as treatment level increases."	"T2R3"	"Number of averted infections going from f=0.5 to f=1"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	3	"Now allow resistance to be generated during treatment (_c~t~_ > 0). Set _c~t~ = 0.2_ for the fraction of resistant generation from treatment. Run the simulation for the treatment levels specified in the previous task, again determine total number infected for each treatment level. On a piece of paper, sketch out the relationship between treatment level and the total number infected in the absence and presence of resistance generation (_c~t~_ = 0, recorded in previous task, and _c~t~_ > 0, recorded here). What do you conclude from that?"	"T3R1"	"Number of averted infections going from f=0.25 to f=0.75"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	3	"Now allow resistance to be generated during treatment (_c~t~_ > 0). Set _c~t~ = 0.2_ for the fraction of resistant generation from treatment. Run the simulation for the treatment levels specified in the previous task, again determine total number infected for each treatment level. On a piece of paper, sketch out the relationship between treatment level and the total number infected in the absence and presence of resistance generation (_c~t~_ = 0, recorded in previous task, and _c~t~_ > 0, recorded here). What do you conclude from that?"	"T3R2"	"Number of averted infections going from f=0 to f=0.5"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	3	"Now allow resistance to be generated during treatment (_c~t~_ > 0). Set _c~t~ = 0.2_ for the fraction of resistant generation from treatment. Run the simulation for the treatment levels specified in the previous task, again determine total number infected for each treatment level. On a piece of paper, sketch out the relationship between treatment level and the total number infected in the absence and presence of resistance generation (_c~t~_ = 0, recorded in previous task, and _c~t~_ > 0, recorded here). What do you conclude from that?"	"T3R3"	"Number of averted infections going from f=0.5 to f=1"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	4	"Set the rate of transmission for resistant hosts to _b~r~ = 0.001_, also increase resistance generation during treatment to _c~t~ = 0.3_.  Keep everything else as previously. Contemplate what these changes mean biologically, and what you should expect from the simulations. Run the model for each of the 5 treatment levels specified above and record the total number infected at each treatment level. Draw them in the same figure you started above. What do you conclude from that?"	"T4R1"	"Number of averted infections going from f=0.25 to f=0.75"	"Rounded_Integer"	"Report the rounded integer. Expect to get some negative values."
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	4	"Set the rate of transmission for resistant hosts to _b~r~ = 0.001_, also increase resistance generation during treatment to _c~t~ = 0.3_.  Keep everything else as previously. Contemplate what these changes mean biologically, and what you should expect from the simulations. Run the model for each of the 5 treatment levels specified above and record the total number infected at each treatment level. Draw them in the same figure you started above. What do you conclude from that?"	"T4R2"	"Number of averted infections going from f=0 to f=0.5"	"Rounded_Integer"	"Report the rounded integer. Expect to get some negative values."
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	4	"Set the rate of transmission for resistant hosts to _b~r~ = 0.001_, also increase resistance generation during treatment to _c~t~ = 0.3_.  Keep everything else as previously. Contemplate what these changes mean biologically, and what you should expect from the simulations. Run the model for each of the 5 treatment levels specified above and record the total number infected at each treatment level. Draw them in the same figure you started above. What do you conclude from that?"	"T4R3"	"Number of averted infections going from f=0.5 to f=1"	"Rounded_Integer"	"Report the rounded integer. Expect to get some negative values."
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	5	"In the previous task, you should have found that as you increase treatment, the total number of infected initially goes down, but then goes up again. Let's try to find the optimal treatment level. Explore different values of _f_ in steps of 0.05 to find the value that gives the lowest total number of infected (of course you don't have to start at f=0, you can explore the space around the area where you know the optimal value is."	"T5R1"	"The treatment level f for which you get the lowest number of total infections"	"Numeric"	""
"DSAIDE_evolution"	"evolution"	"Evolution of Drug Resistance"	6	"The general idea explored here is that if you have a situation where resistance might emerge, it reduces the effectiveness of a treatment and might mean that sometimes more treatment is not better. The details depend on the fitness of the different infection compartments (in our model represented by the parameters _b~i~_ and _g~i~_) and the speed at which resistance can emerge either without or with treatment, which in our model is governed by the parameters _c~u~_ and _c~t~_. Explore how different values for those parameters impact the usefulness of treatment. As you change parameter values, try to connect them to biology, i.e. try to understand what it would mean in the real world if certain parameters had certain values."	"T6R1"	"Nothing"	"None"	""
