"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	1	"Start with 1000 susceptible individuals and 1 initially infected host, no recovered. Set simulation duration to 100 days, start time and time step can remain at 0 and 0.1. Set recovery rate _g_=0.5 per day, and infectiousness _b_=0.001. You will get an outbreak of some - currently unspecified - infectious disease. If you did it correctly, your outbreak should end with around 203 uninfected individuals still remaining. Also take a look at the final number of infected. You'll see that it is at _3E-9_, i.e. much less than 1 but not 0. That's the issue discussed above in the notes, that for ODE models, numbers can drop below 1, and then can slowly go to 0 but not fully reach 0. Often, this biologically unreasonable feature of these kinds of models can be ignored, but sometimes it matters. You will learn more about this when you work through the stochastic model apps."	"T1R1"	"Total number of recovered at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	2	"Switch the plotting to have x-axis, y-axis or both plotted on a log scale. Leave all other settings as before. Note that while the look of the plot changes a lot, nothing has changed about the underlying simulation. The results are exactly the same in each case, only plotted differently. This is something to be aware of when you see plots in papers or produce your own. The best plot to use is the one that shows results of interest in the clearest form. Usually, the x-axis is linear and the y-axis is either linear or logarithmic. Try again switching between ggplot and plotly as plotting engine. Play around a bit with plotly. It has a number of interactive features, such as zooming, reading off values from a graph, turning on/off specific lines, etc. Those features will likely be useful as you go through the different apps in DSAIDE, thus I encourage you to explore the plotly functionality somewhat."	"T2R1"	"Nothing"	"None"	""
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	3	"From the graph, get a (rough) estimate of the day at which the outbreak peaks. That's easiest done when you make a plotly plot. Contemplate the fact that the outbreak ends even though there are still susceptible remaining, i.e. not everyone got infected. Do you find that surprising? How could you maybe explain that? This topic is discussed in more detail in the reproductive number app. 

You have no infected left at the end of the outbreak. Figure out how you can use the information from either the susceptibles or recovered left at the end of the outbreak to figure out how many people in total got infected during the outbreak. To that end, think about how individuals move through the different compartments in this model. What can happen to those in the S/I/R compartments as the outbreak progresses? Where are they at the end of the outbreak? Use either information about the number of individuals in S or R at the beginning and end of the outbreak to determine the total (cumulative) number of individuals that got infected during the outbreak.

Rerun the simulation, with the same values you just had. Does anything change? Why (not)?"	"T3R1"	"Day the outbreak peaked"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	3	"From the graph, get a (rough) estimate of the day at which the outbreak peaks. That's easiest done when you make a plotly plot. Contemplate the fact that the outbreak ends even though there are still susceptible remaining, i.e. not everyone got infected. Do you find that surprising? How could you maybe explain that? This topic is discussed in more detail in the reproductive number app. 

You have no infected left at the end of the outbreak. Figure out how you can use the information from either the susceptibles or recovered left at the end of the outbreak to figure out how many people in total got infected during the outbreak. To that end, think about how individuals move through the different compartments in this model. What can happen to those in the S/I/R compartments as the outbreak progresses? Where are they at the end of the outbreak? Use either information about the number of individuals in S or R at the beginning and end of the outbreak to determine the total (cumulative) number of individuals that got infected during the outbreak.

Rerun the simulation, with the same values you just had. Does anything change? Why (not)?"	"T3R2"	"Total/cumulative number infected  (compute as described in text)"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	4	"Set _Models to run_ to _both_, which runs and shows both the continuous-time and discrete-time models. Start with a discrete-time step of 0.01. Leave all other settings as before. Run the simulation, see what you get. You should see the results from the 2 models essentially on top of each other and barely distinguishable."	"T4R1"	"Number of susceptible at end of simulation for continuous-time model"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	4	"Set _Models to run_ to _both_, which runs and shows both the continuous-time and discrete-time models. Start with a discrete-time step of 0.01. Leave all other settings as before. Run the simulation, see what you get. You should see the results from the 2 models essentially on top of each other and barely distinguishable."	"T4R2"	"Number of susceptible at end of simulation for discrete-time model"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	5	"Now try different values for _dt_. Leave all other settings as before. You should notice that as _dt_ gets larger, the differences between discrete-time and continuous-time models increase. At some point when the time-step gets too large, the discrete-time simulation 'crashes' and you get an error message. This doesn't impact the continuous/ODE simulation since it chooses its time-step during the simulation internally and _dt_ only affects the times for which the results are returned."	"T5R1"	"Number of susceptible left at end of simulation for continuous-time model dt=1"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	5	"Now try different values for _dt_. Leave all other settings as before. You should notice that as _dt_ gets larger, the differences between discrete-time and continuous-time models increase. At some point when the time-step gets too large, the discrete-time simulation 'crashes' and you get an error message. This doesn't impact the continuous/ODE simulation since it chooses its time-step during the simulation internally and _dt_ only affects the times for which the results are returned."	"T5R2"	"Number of susceptible left at end of simulation for discrete-time model dt=1"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	6	"Go back to running the ODE model only. Set all parameters as described in task 1. Then increase the rate of recovery by a factor of four, i.e. set it to g=2. (What duration of the infectious period does that translate to?) Contemplate what you expect to find for the increased recovery rate before you run the simulation. Then run the simulation. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T6R1"	"Number of susceptible at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	6	"Go back to running the ODE model only. Set all parameters as described in task 1. Then increase the rate of recovery by a factor of four, i.e. set it to g=2. (What duration of the infectious period does that translate to?) Contemplate what you expect to find for the increased recovery rate before you run the simulation. Then run the simulation. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T6R2"	"Number of recovered at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	6	"Go back to running the ODE model only. Set all parameters as described in task 1. Then increase the rate of recovery by a factor of four, i.e. set it to g=2. (What duration of the infectious period does that translate to?) Contemplate what you expect to find for the increased recovery rate before you run the simulation. Then run the simulation. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T6R3"	"Do you see an outbreak? (Yes/No)"	"Logical"	"Report Yes or No"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	7	"Increase the transmission rate by a factor of 4, leave everything else as previously. How do you expect the results to change? (Try to make your prediction as precise/quantitative as you can.) Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Compare the values for number susceptible/recovered at end of simulation and the peak of the outbreak to the values you found in the initial tasks. Hint: You should see that the _magnitude_ of the outbreak is essentially the same, but the _timing_ has changed."	"T7R1"	"Number of susceptible at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	7	"Increase the transmission rate by a factor of 4, leave everything else as previously. How do you expect the results to change? (Try to make your prediction as precise/quantitative as you can.) Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Compare the values for number susceptible/recovered at end of simulation and the peak of the outbreak to the values you found in the initial tasks. Hint: You should see that the _magnitude_ of the outbreak is essentially the same, but the _timing_ has changed."	"T7R2"	"Number of recovered at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	7	"Increase the transmission rate by a factor of 4, leave everything else as previously. How do you expect the results to change? (Try to make your prediction as precise/quantitative as you can.) Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Compare the values for number susceptible/recovered at end of simulation and the peak of the outbreak to the values you found in the initial tasks. Hint: You should see that the _magnitude_ of the outbreak is essentially the same, but the _timing_ has changed."	"T7R3"	"Do you see an outbreak? (Yes/No)"	"Logical"	"Report Yes or No"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	7	"Increase the transmission rate by a factor of 4, leave everything else as previously. How do you expect the results to change? (Try to make your prediction as precise/quantitative as you can.) Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Compare the values for number susceptible/recovered at end of simulation and the peak of the outbreak to the values you found in the initial tasks. Hint: You should see that the _magnitude_ of the outbreak is essentially the same, but the _timing_ has changed."	"T7R4"	"Day at which outbreak peaked"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	8	"Double the number of susceptibles, _S~0~_, leave everything else as you just had. How do you expect the results to change? Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T8R1"	"Number of susceptible at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	8	"Double the number of susceptibles, _S~0~_, leave everything else as you just had. How do you expect the results to change? Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T8R2"	"Number of recovered at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	8	"Double the number of susceptibles, _S~0~_, leave everything else as you just had. How do you expect the results to change? Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T8R3"	"Do you see an outbreak? (Yes/No)"	"Logical"	"Report Yes or No"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	8	"Double the number of susceptibles, _S~0~_, leave everything else as you just had. How do you expect the results to change? Run the simulation with these new parameter settings. Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?"	"T8R4"	"Day at which outbreak peaked"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	9	"Keep playing around with changing any of the parameters and starting conditions. Every time, think about what you expect to get, then run the simulation, compare your expectations with the results. Then make sense of it. What is the minimum and maximum number of outbreaks you can get? Why is that?"	"T9R1"	"Minimum number of outbreaks you can get"	"Integer"	"Report an integer"
"DSAIDE_basicsir"	"basicsir"	"Basic SIR Model"	9	"Keep playing around with changing any of the parameters and starting conditions. Every time, think about what you expect to get, then run the simulation, compare your expectations with the results. Then make sense of it. What is the minimum and maximum number of outbreaks you can get? Why is that?"	"T9R2"	"Maximum number of outbreaks you can get"	"Integer"	"Report an integer"
