"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	1	" Start with 1000 susceptible hosts and 1 infected host of type one. 200 susceptible hosts and 1 infected host of type two. Simulation duration approximately 5 years. Assume that transmission from host 1 to host 1 is _b~11~ = 0.002_, from host 2 to host 2 is _b~22~ = 0.01_. No transmission from one host type to the other _b~12~ = 0_ and _b~21~ = 0_. Assume that the duration of the infectious period is 1 month long for both types of hosts (i.e. same recovery rate). No waning immunity. Run the simulation and ensure you get outbreaks in both populations with approximately 20% susceptibles left at the end."	"T1R1"	"Number susceptible of type 1 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	1	" Start with 1000 susceptible hosts and 1 infected host of type one. 200 susceptible hosts and 1 infected host of type two. Simulation duration approximately 5 years. Assume that transmission from host 1 to host 1 is _b~11~ = 0.002_, from host 2 to host 2 is _b~22~ = 0.01_. No transmission from one host type to the other _b~12~ = 0_ and _b~21~ = 0_. Assume that the duration of the infectious period is 1 month long for both types of hosts (i.e. same recovery rate). No waning immunity. Run the simulation and ensure you get outbreaks in both populations with approximately 20% susceptibles left at the end."	"T1R2"	"Number susceptible of type 2 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	2	"Set _b~22~ = 0.02_. Rest as before. Run the simulation. You should get the same outbreak as before among type 1 hosts, a larger outbreak among type 2 hosts. 
For our current choice of parameters, more specifically the transmission rates, it is ok to define and compute separate R~0~ for the two populations. Contemplate why it is ok to do so and compute the two R~0~ values. Use what you learned about R~0~ to compute a theoretical value, then check with the simulation using the final size equation."	"T2R1"	"R0 for type 1 hosts"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	2	"Set _b~22~ = 0.02_. Rest as before. Run the simulation. You should get the same outbreak as before among type 1 hosts, a larger outbreak among type 2 hosts. 
For our current choice of parameters, more specifically the transmission rates, it is ok to define and compute separate R~0~ for the two populations. Contemplate why it is ok to do so and compute the two R~0~ values. Use what you learned about R~0~ to compute a theoretical value, then check with the simulation using the final size equation."	"T2R2"	"R0 for type 2 hosts"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	3	"Set _b~11~ = 0.001_. Rest as in previous task. Run the simulation. You should get the same outbreak as before among type 2 hosts, but no real outbreak among type 1 hosts (though a few infections will occur). Compute the R~0~ values for these settings and convince yourself that the theoretical values and simulation results agree."	"T3R1"	"R0 for type 1 hosts"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	3	"Set _b~11~ = 0.001_. Rest as in previous task. Run the simulation. You should get the same outbreak as before among type 2 hosts, but no real outbreak among type 1 hosts (though a few infections will occur). Compute the R~0~ values for these settings and convince yourself that the theoretical values and simulation results agree."	"T3R2"	"R0 for type 2 hosts"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	4	"Now set the transmission rate to host 1 from host 2 _b~12~ = 0.001_. Everything else unchanged. Run the simulation. You should see an outbreak in both populations. This is an example of a (small) core group driving an outbreak in the larger group. Now that the two groups interact, the simple individual equations for _R~0~_ do not apply anymore. One can compute an overall _R~0~_ for the joint populations, but that is a bit involved and beyond what we want to do here. For more details on that, see e.g. [@keeling07]."	"T4R1"	"Number susceptible of type 1 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	4	"Now set the transmission rate to host 1 from host 2 _b~12~ = 0.001_. Everything else unchanged. Run the simulation. You should see an outbreak in both populations. This is an example of a (small) core group driving an outbreak in the larger group. Now that the two groups interact, the simple individual equations for _R~0~_ do not apply anymore. One can compute an overall _R~0~_ for the joint populations, but that is a bit involved and beyond what we want to do here. For more details on that, see e.g. [@keeling07]."	"T4R2"	"Number susceptible of type 2 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	5	"Let's test the idea that the small core group is driving the outbreaks and it's not just because we introduced cross-transmission and that feature alone led to outbreaks in both populations. To do so, set _b~12~_ to 0 and instead set _b~21~ = 0.001_. If it was just due to cross-transmission, we should probably see outbreaks in both populations. If it was a more infectious/transmissible (i.e. higher _R~0~_) core-group driving the outbreak in the less transmissible group, we should not see an outbreak. Run the simulation and interpret the results."	"T5R1"	"Nothing"	"None"	""
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	6	"Now set transmission rates to _b~11~_ = _b~22~_ = 0 and _b~12~_ = 0.01, _b~21~_ = 0.002. Note that these are the same values for the transmission terms as in task 1, but we now there is no transmission among individuals of the same type and non-zero transmission between types. Contemplate what you expect to see, run the simulation, see if your expectations are confirmed. Compare the results to those from task 1."	"T6R1"	"Number susceptible of type 1 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	6	"Now set transmission rates to _b~11~_ = _b~22~_ = 0 and _b~12~_ = 0.01, _b~21~_ = 0.002. Note that these are the same values for the transmission terms as in task 1, but we now there is no transmission among individuals of the same type and non-zero transmission between types. Contemplate what you expect to see, run the simulation, see if your expectations are confirmed. Compare the results to those from task 1."	"T6R2"	"Number susceptible of type 2 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	7	"Note that things are not symmetric here when it comes to values of cross-transmission. Explore this by switching the values for the cross-transmission terms, such that now _b~12~_ = 0.002, _b~21~_ = 0.01. Leave everything else as in the previous task. Run the simulation and compare the results to those from the previous task."	"T7R1"	"Number susceptible of type 1 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	7	"Note that things are not symmetric here when it comes to values of cross-transmission. Explore this by switching the values for the cross-transmission terms, such that now _b~12~_ = 0.002, _b~21~_ = 0.01. Leave everything else as in the previous task. Run the simulation and compare the results to those from the previous task."	"T7R2"	"Number susceptible of type 2 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	8	"You have previously encountered the idea that transmission occurs only between hosts of different types, namely in the vector-borne transmission app. There we thought of the different types as different species, e.g. humans and mosquitoes. But it could also be among different types of the same hosts. For instance this type of transmission could represent a sexually transmitted disease in a heterosexual population, with the 2 types of hosts being females and males. Many sexually transmitted infections do not produce life-long immunity, re-infection is possible and not uncommon. Let's explore this. Set the number of susceptibles for both populations to 1000, 1 infected in each population. Set b~12~ = 0.002, b~21~ = 0.001. Set the other transmission terms to 0. Leave recovery rates as before. Then turn on waning immunity, assume it has an average duration of 5 months for each population (i.e. rates _w~1~_ and _w~2~_ need to be the inverse of 5 months). Run the simulation for 10 years. Confirm that you reach a steady state, with around 667 susceptible of type 1 at the end."	"T8R1"	"Number susceptible of type 2 left at end of simulation"	"Rounded_Integer"	"Report the rounded integer"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	9	"In the previous task, the steady state values for S/I/R for the 2 populations were different. Think about why that is the case. Try to find the value for b~21~ for which the 2 populations reach the same steady state. Then explore different values for both transmission rates and see how that impact the outcomes. Consider what that means for a real infectious disease. Would you expect transmission rates to be the same or not? What does it depend on?"	"T9R1"	"Value for b21 at which steady state values for both populations are the same."	"Numeric"	"Report the exact value"
"DSAIDE_hostheterogeneity"	"hostheterogeneity"	"Host Heterogeneity"	10	"Keep exploring. You can continue the exploration from the previous task by altering other parameters for the 2 types. You might for instance want to consider some real ID where accounting for 2 types of hosts is important, e.g. some type of STI. You can try to go to the literature and see if you can find parameter values for the ID of interest and explore potential patterns of that ID with the simulation."	"T10R1"	"Nothing"	"None"	""
