"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	1	"Run the model with the default settings. Confirm that you get a single infection with sensitive virus, with a virus peak of around 1.5M virions. You get also get a few resistant virus particles that are generated, but they don't take off and lead to an infection. Try to figure out why that is so."	"T1R1"	"Peak of resistant virus"	"Integer"	"Report the integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	2	"Set fraction of restant mutants created to _m = 0.01_. That means about 1% of all virions produced by a cell infected with sensitive virus are resistant mutants. Run the simulation. 


You'll see higher values for resistant virus, but still not as high as the sensitive virus. This could be just by chance. Therefore, let's run more than one scenario. Set number of simulations to 20 and run them. 


You'll see that for all runs, the resistant mutant does not grow much. One reason for this is that the sensitive virus has an early start. By the time the resistant one is generated, it can't catch up anymore. Let's change this by turning off resistant mutant generation and instead start with 10 resistant and 10 susceptible virions. Run 20 iterations again. 


You'll see that the resistant virus now reaches higher levels, but still not as high as the sensitive. Why?"	"T2R1"	"Average peak of sensitive infected cells for equal virus starting values, no mutation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	2	"Set fraction of restant mutants created to _m = 0.01_. That means about 1% of all virions produced by a cell infected with sensitive virus are resistant mutants. Run the simulation. 


You'll see higher values for resistant virus, but still not as high as the sensitive virus. This could be just by chance. Therefore, let's run more than one scenario. Set number of simulations to 20 and run them. 


You'll see that for all runs, the resistant mutant does not grow much. One reason for this is that the sensitive virus has an early start. By the time the resistant one is generated, it can't catch up anymore. Let's change this by turning off resistant mutant generation and instead start with 10 resistant and 10 susceptible virions. Run 20 iterations again. 


You'll see that the resistant virus now reaches higher levels, but still not as high as the sensitive. Why?"	"T2R2"	"Average peak of resistant infected cells for equal virus starting values, no mutation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	3	"You probably figured out that the resistant virus is not growing as much because we gave it a fitness cost. Let's remove that and set _f = 0._ Keep everything as before, run again. 


You should now find that the two strains produce on average similar sized infections. Though, for any one simulation run, one strain or the other usually dominates. You can explore this by running one simulation at a time for different random seeds."	"T3R1"	"Average peak of sensitive infected cells for equal virus starting values, _m_ = 0, _f_ = 0"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	3	"You probably figured out that the resistant virus is not growing as much because we gave it a fitness cost. Let's remove that and set _f = 0._ Keep everything as before, run again. 


You should now find that the two strains produce on average similar sized infections. Though, for any one simulation run, one strain or the other usually dominates. You can explore this by running one simulation at a time for different random seeds."	"T3R2"	"Average peak of resistant infected cells for equal virus starting values, _m_ = 0, _f_ = 0"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	4	"We established that in the absence of a drug, if a resistant strain has a fitness cost, it is unlikley to out-compete the drug sensitive strain. A drug can change the fitness balance and by suppressing the generation of sensitive virus, making the resistant virus more competitive. Let's explore this. 


Reset all inputs. Run 20 simulations just to confirm there is not much drug resistant virus. Then, set drug efficacy to 0.6. Run the simulation. 


You'll find that the resistant strain becomes much more competitive. Next, try a drug with _e = 0.9_. 


You should find that for some simulation runs, the drug is so good at quickly removing the sensitive virus that there is no time to generate resistant virus; thus, no infection occurs with either type. This points to a trade-off: At low drug efficacy, the sensitive strain doesn't have much of a fitness loss and still can outcompete the resistant strain, so no resistance emerges. At very high drug efficacy, the drug might be able to prevent replication of the sensitive virus quickly enough to prevent generation of resistant virus in the first place. At intermediate levels, the resistant strain has the best chance to emerge. The drug is not strong enough to reduce susceptible virus replication enough to prevent resistance generation, but it is strong enough to give the resistant strain a fitness advantage once it has been generated."	"T4R1"	"Average peak of sensitive infected cells for _e_ = 0.6"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	4	"We established that in the absence of a drug, if a resistant strain has a fitness cost, it is unlikley to out-compete the drug sensitive strain. A drug can change the fitness balance and by suppressing the generation of sensitive virus, making the resistant virus more competitive. Let's explore this. 


Reset all inputs. Run 20 simulations just to confirm there is not much drug resistant virus. Then, set drug efficacy to 0.6. Run the simulation. 


You'll find that the resistant strain becomes much more competitive. Next, try a drug with _e = 0.9_. 


You should find that for some simulation runs, the drug is so good at quickly removing the sensitive virus that there is no time to generate resistant virus; thus, no infection occurs with either type. This points to a trade-off: At low drug efficacy, the sensitive strain doesn't have much of a fitness loss and still can outcompete the resistant strain, so no resistance emerges. At very high drug efficacy, the drug might be able to prevent replication of the sensitive virus quickly enough to prevent generation of resistant virus in the first place. At intermediate levels, the resistant strain has the best chance to emerge. The drug is not strong enough to reduce susceptible virus replication enough to prevent resistance generation, but it is strong enough to give the resistant strain a fitness advantage once it has been generated."	"T4R2"	"Average peak of resistant infected cells for _e_ = 0.6"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_drugresistance"	"drugresistance"	"Influenza Antivirals and Drug Resistance"	5	"Keep exploring how different levels of fitness cost, _f_, rate of resistance generation, _m_, and drug efficacy, _e_, change the competition and outcome. If you are comfortable with a bit of coding, namely the Level 2 approach described in the package tutorial, you could write a loop over different drug efficacy values and for each value, run a number of simulations and record for how many the resistant strain dominates. You will find that resistance emergence is most likely at intermediate drug efficacy levels. Note that, for this model, the start of the treatment occurs at the beginning. In a more realistic model, one would likely assume that drug treatment starts some time after the infection has started."	"T5R1"	"Nothing"	"None"	""
