"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIDE_fitnoro"	"fitnoro"	"Fitting Norovirus data"	1	"Take a look at the inputs and outputs for the app. It is similar to the _Fitting influenza data_ app (which you should do before this one). Each model variant fits parameters _b_ and _g_. Model variant 2 also fits a common source infection rate _n_. Model variant 3 additionally fits times _t~1~_ and _t~2~_ at which infection from the common source starts and stops (i.e., is larger than 0). The best fit estimates are shown under the figure, together with the SSR and AICc. For simplicity, the lower and upper bounds for the on/off times _t1_ and _t2_ are set inside the simulator function to the beginning and end of the data time-series. As a result, they can't be adjusted through the user interface. To find out more about the data, see _Further Resources_ or `help(norodata)`. You can play around a bit with the app before starting with the next task."	"T1R1"	"Nothing"	"None"	""
"DSAIDE_fitnoro"	"fitnoro"	"Fitting Norovirus data"	2	"The setting from which the data comes had a total of 288 susceptible individuals. Use that as starting value for the susceptibles. Assume 1 initial infected, no recovered. We'll start by fitting model variant 1, which fits parameters  _b_ and _g_. You could set _b_ and _g_ to pretty much any starting value and try to run a large number of iterations with the 3 different solvers until you get a good fit. As you learned in the flu fitting app, that doesn't always work, starting values are often important. To find good starting values, you can manually change starting values for _b_ and _g_, run a single iteration and visually compare model and data. If model and data are reasonable close, you could run the optimizer for more iterations. In theory there is a single best fit. However, as discussed in the flu fitting app, optimizers can sometimes get stuck on good, but not best, fits. So it might be for certain settings you don't end up with a good fit. Some trial and error is usually required. The best fit for this model is (as far as I know) one with an SSR = 1218. Try various combinations of solver type, iterations and starting values until you reach this SSR. Note that for this and the following tasks, you might need to push iterations into the thousands. Record the best fit value for the rate of recovery. Also make a note of the AICc, you'll need it later."	"T2R1"	"Estimate for best-fit value of parameter g (recovery rate)"	"Numeric"	"Round to two significant digits, report as non-scientic notation (as X.YZ)"
"DSAIDE_fitnoro"	"fitnoro"	"Fitting Norovirus data"	3	"Now switch to model variant 2, which also fits parameter _n_. Play around with different starting values for the parameters, different optimizers and different numbers of iterations and see what the best fit is you can find. You'll likely notice that getting a good fit is trickier now that you added one more parameter to be estimated. That's a general occurence, more parameters make it harder to obtain estimates. The best fit I was able to find is identical to model 1, i.e., the optimizer sets the rate of infection from an external source to _n=0_. This, of course, gives the same SSR. However, since we have more parameters now, the AICc is larger. Based on comparison of the AICc values for the 2 models we explored so far, we would conclude that model 1 (no additional external source of infection) is a more consistent or parsimonious description of the data."	"T3R1"	"AIC for best fit"	"Numeric"	"Round to two significant digits, report as non-scientic notation (as X.YZ)"
"DSAIDE_fitnoro"	"fitnoro"	"Fitting Norovirus data"	4	"Now switch to model variant 3, which also fits parameters _t~1~_ and _t~2~_. See what best fit you can find. Note that the starting value for _t~1~_ must be 8 days (the start of the data) or greater, otherwise you'll get an error. I was able, with 10,000 iterations and good starting conditions, to get SSR values <20. My lowest was around 17, you might be able to find an even better fit (lower SSR). Try until you find one with an SSR of 20 or less. Sometimes using the best-fit results from a run as new starting values for a different run while switching between optimizers can help with the _getting stuck_ problem. An SSR of <20 is clearly lower than that for model 1 and 2. AICc will give us an indication of this gain in better fit justifies adding the additional 3 parameters (_n_,  _t~1~_ and _t~2~_). Look at the AICc for your best fit (one for which SSR<20). Is the AICc value lower than that for model 1 and 2? If yes, it means model 3 is favored, otherwise model 1 is favored (we already ruled out model 2)."	"T4R1"	"Based on AICc, model 3 is favored (TRUE/FALSE)"	"Logical"	"Report TRUE or FALSE"
