"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	1	"Set the model parameters such that it corresponds to the following setting: 1000 susceptible hosts and vectors, 1 initially symptomatic host, all other compartments start at 0. Assume that there are no births or natural deaths occurring for either hosts or vectors. Assume that only symptomatic individuals transmit, at a rate of 0.002. All other transmission rates should be 0. Assume that the duration of the symptomatic period is 1 month long, the duration of the presymptomatic period is half a month long. Assume that there are no asymptomatic infections. You can, therefore, set the rate of recovery of asymptomatics to anything, it doesn't matter because nobody will be asymptomatic. Assume that no environmental shedding and decay occurs. Assume nobody dies due to disease, and immunity does not wane.  Simulation duration approximately 5 years. With parameters set to correspond to the scenario just described, run the simulation and ensure you get a single outbreak with ~20% susceptibles left at the end."	"T1R1"	"Peak/Maximum number of infected symptomatic (I)."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	2	"Let's now assume that 50% of infected hosts will be asymptomatic (_f_) and that the duration of the asymptomatic stage (_gA_) is the same as the symptomatic stage (_gI_). Also, assume that asymptomatic infected are half as infectious as symptomatic infected, and that pre-symptomatic are as infectious as symptomatic. Run the simulation, you should get an outbreak with around 107 susceptibles left. Now, we will envision a scenario where we can only detect and isolate individuals that show symptoms. Assume that isolation reduces transmission rates by half. Implement such a scenario, run the simulation. 

If you are up for a small, optional, challenge, compute R~0~ for this scenario. It will be the sum of transmission coming from all 3 infected compartments (P, A, I). You will also need to make sure you multiply _A_ and _I_ by their respective fractions (_f_ and _1-f_) otherwise you would be double-counting. Other than that it follows the standard procedure you learned about and you can compare what you get from doing the math with the final size equation. You can then use your R~0~ equation for the next following tasks."	"T2R1"	"Number susceptibles remaining at end of isolation simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	3	"Set parameter values back to what they were at the start of the previous task. Now, we envision a scenario where we can isolate everyone who has become infected, independent of symptom status. We assume this reduces transmission by half for anyone isolated. Implement such a scenario, again run the simulation and record the number of susceptibles left at the end."	"T3R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	4	"Set parameter values back to what they were at the start of the previous task before you started changing them. Now assume that we can administer a drug. It will likely only be given to symptomatic individuals. First assume that the drug reduces the infectiousness of symptomatic individuals by half. Run the simulation. Convince yourself that this has the same effect as assuming isolation of of symptomatic individuals above. Now assume that the drug also reduces the duration of the symptomatic period from a month to half a month. What do you expect this shortening of the symptomatic period will do? Run the simulation to check."	"T4R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	5	"Set parameter values back to what they were at the start of the previous task. Then change transmission terms such that bP=0.001, bA=0.002, bI=0.001. Can you think of a pathogen where asymptomatic individuals might transmit more than symptomatic individuals? Some STI might fall into this category. Pathogens that require close contact could also be of that type if symptomatic are at home sick and can't spread the disease. Run the simulation with those settings, you should see an outbreak with around 203 susceptible left. Now assume that we can administer a drug which reduces symptoms in people, but doesn't change their transmission rates. We mimic this by assuming the fraction that are asymptomatic increases to 75%. What do you expect this will do to the overall outbreak size? Run the simulation to check."	"T5R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	6	"Now let's consider a pathogen that has some environmental transmission component. Set everything as in task 1. Then turn on environmental shedding by symptomatic individuals by setting pI=2 and set decay of pathogen in environment to c=1. Run the simulation. You should see E come up and go back down similar to the I curve. Make a note of the maximum value for E, it should be around 194. Since we haven't turned on transmission from the environment yet, the outbreak size should be the same as in task 1. Now set bE=0.0005. You will see a larger outbreak (less susceptible hosts remaining) and also higher levels of E. Why do you see a larger outbreak and more E now? Think about how the pathogen moves through the system.

Again, if you are up for an optional challenge and work on your R~0~ skills, determine R~0~ for this scenario. You can use the discussion in the _Environmental Transmission_ app to guide you. Since here we have transmission both through environment and direct transmission, R~0~ will be the sum of transmission coming from all 3 infected compartments (P, A, I) and the environement E. Though for this task, we have no transmission from P and A, so the sum is only the transmission terms from I and E."	"T6R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	6	"Now let's consider a pathogen that has some environmental transmission component. Set everything as in task 1. Then turn on environmental shedding by symptomatic individuals by setting pI=2 and set decay of pathogen in environment to c=1. Run the simulation. You should see E come up and go back down similar to the I curve. Make a note of the maximum value for E, it should be around 194. Since we haven't turned on transmission from the environment yet, the outbreak size should be the same as in task 1. Now set bE=0.0005. You will see a larger outbreak (less susceptible hosts remaining) and also higher levels of E. Why do you see a larger outbreak and more E now? Think about how the pathogen moves through the system.

Again, if you are up for an optional challenge and work on your R~0~ skills, determine R~0~ for this scenario. You can use the discussion in the _Environmental Transmission_ app to guide you. Since here we have transmission both through environment and direct transmission, R~0~ will be the sum of transmission coming from all 3 infected compartments (P, A, I) and the environement E. Though for this task, we have no transmission from P and A, so the sum is only the transmission terms from I and E."	"T6R2"	"Max/peak of E during simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	7	"Let's repeat the previous task, with only environmental transmission possible. Start by setting transmission bI=0 and bE=0, leave everything else unchanged. Run the simulation. Nothing should happen. The single infected person sheds a bit into the environment, but since we haven't turned on transmission from the environment yet, nothing else happens. If you zoom into the plot (with plotly) to the beginning, you see I drop to 0 and E come up a little bit and then drop as well. Now set bE=0.002. You will see an outbreak, now driven entirely through environmental transmission (compared to a mix of direct and environmental transmission in our previous task)."	"T7R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	7	"Let's repeat the previous task, with only environmental transmission possible. Start by setting transmission bI=0 and bE=0, leave everything else unchanged. Run the simulation. Nothing should happen. The single infected person sheds a bit into the environment, but since we haven't turned on transmission from the environment yet, nothing else happens. If you zoom into the plot (with plotly) to the beginning, you see I drop to 0 and E come up a little bit and then drop as well. Now set bE=0.002. You will see an outbreak, now driven entirely through environmental transmission (compared to a mix of direct and environmental transmission in our previous task)."	"T7R2"	"Max/peak of E during simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	8	"Time to apply control. Keep settings as you just had them, and assume an intervention is able to remove pathogen faster from the environment (e.g. through better waste removal/treatment). Double the rate of pathogen decay in the environment."	"T8R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	8	"Time to apply control. Keep settings as you just had them, and assume an intervention is able to remove pathogen faster from the environment (e.g. through better waste removal/treatment). Double the rate of pathogen decay in the environment."	"T8R2"	"Max/peak of E during simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	9	"Set pathogen decay rate back to what it was at the beginning of the previous task. Now we assume that we have an intervention that reduces shedding of pathogen by infected (e.g. through better access to sanitation). To mimic this, halve the rate of environmental shedding by symptomatic individuals. Run the simulation."	"T9R1"	"Number susceptibles remaining at end of simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	9	"Set pathogen decay rate back to what it was at the beginning of the previous task. Now we assume that we have an intervention that reduces shedding of pathogen by infected (e.g. through better access to sanitation). To mimic this, halve the rate of environmental shedding by symptomatic individuals. Run the simulation."	"T9R2"	"Max/peak of E during simulation."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	10	"As you compare the previous 2 interventions (increase in _c_ or reduction in _pI_), which one was better? Explore a bit more how different choices of those two parameter affect the outbreak. If you computed R~0~ for this scenario, you can directly read off from the equation how those two parameters affect R~0~ and thus outbreak size."	"T10R1"	"Nothing"	"None"	""
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	11	"Now we'll switch to vector-borne transmission. Set everything as in task 1. Also set initial number of susceptible and infected vectors to 1000 and 1. Assume that direct transmission between hosts does not occur, so all those rates should be 0. Same for environmental transmission. Set transmission from host to vector and vector to host to 0.002. Assume that vectors (say mosquitoes) live for half a month. Set vector birth rate such that vector population balances at 1000 in the absence of any infections. Run the simulation for 10 years. You should end up with around 228 susceptibles at the end of the simulation. Now consider some vector control measures. Assume we sprayed against mosquitoes and it reduced the population size by 90%. Set the initial susceptible vector population to that reduced value. Run the simulation. You should find that this does not have much of a long-term impact because the vector popoulation quickly rebounds."	"T11R1"	"Number susceptible hosts remaining at end of simulation with control."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	11	"Now we'll switch to vector-borne transmission. Set everything as in task 1. Also set initial number of susceptible and infected vectors to 1000 and 1. Assume that direct transmission between hosts does not occur, so all those rates should be 0. Same for environmental transmission. Set transmission from host to vector and vector to host to 0.002. Assume that vectors (say mosquitoes) live for half a month. Set vector birth rate such that vector population balances at 1000 in the absence of any infections. Run the simulation for 10 years. You should end up with around 228 susceptibles at the end of the simulation. Now consider some vector control measures. Assume we sprayed against mosquitoes and it reduced the population size by 90%. Set the initial susceptible vector population to that reduced value. Run the simulation. You should find that this does not have much of a long-term impact because the vector popoulation quickly rebounds."	"T11R2"	"Number susceptible vectors remaining at end of simulation with control."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	12	"Instead of killing vector populations, we now consider a reduction in transmission, e.g. due to the use of bed nets. Set everything as at the beginning of the previous task. Assume that an intervention reduces transmission _to_ vectors to 0.0015, leave the transmission from vectors unchanged. Run the simulation, observe. Now assume that an intervention only reduces transmission _from_ vectors to 0.0015, the rate _to_ vectors now remains at 0.002. Run the simulation, observe. Finally, assume that the intervention reduces transmission both to and from vectors to 0.0015. Run the simulation, observe."	"T12R1"	"Number susceptible hosts remaining at end of simulation with both transmission rates reduced."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	12	"Instead of killing vector populations, we now consider a reduction in transmission, e.g. due to the use of bed nets. Set everything as at the beginning of the previous task. Assume that an intervention reduces transmission _to_ vectors to 0.0015, leave the transmission from vectors unchanged. Run the simulation, observe. Now assume that an intervention only reduces transmission _from_ vectors to 0.0015, the rate _to_ vectors now remains at 0.002. Run the simulation, observe. Finally, assume that the intervention reduces transmission both to and from vectors to 0.0015. Run the simulation, observe."	"T12R2"	"Number susceptible vectors remaining at end of simulation with both transmission rates reduced."	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_idcontrolcomplex"	"idcontrolcomplex"	"Complex Control Scenarios"	13	"While you just worked through a lot of tasks, there are still model parameters and thus scenarios that we haven't touched on. We didn't explore impact of disease-induced death, waning immunity, or presence of all three modes of transmission (though I'm not sure what ID that would apply to). All of those settings, and the impact of different interventions, could be explored. If you have a favorite pathogen, you could try to set parameters in this model to mimic it and explore different interventions and their impact."	"T13R1"	"Nothing"	"None"	""
