## What to do

The tasks below are described in a way that assumes everything is in units of DAYS (rate parameters, therefore, have units of inverse days). If any quantity is not given in those units, you need to convert it first (e.g. if it says a week, you need to convert it to 7 days).

### Task 1:

- Set the model parameters such that it corresponds to the following setting:
  - A population size, S0, of 1000, 1 initially infected presymptomatic host, P0, simulation duration tmax= 200 days.
  - Assume that only symptomatic individuals transmit, at rate bI = 0.001.
  - Assume that the duration of the presymptomatic, asymptomatic and symptomatic periods are all 5 days long. (Hint: The parameters gP, gA, and gI are the inverses of these periods.)
  - Assume that there are no asymptomatic infections, f=0, and nobody dies due to disease d=0.
- With parameters set to correspond to the scenario just described, run the simulation.
- Record the number and fraction of susceptible/infected/recovered remaining at the end of the outbreak.
- Check the results with the assumptions for the model and make sure they agree (you shouldn’t get any deaths, no asymptomatics, etc.)
- From the graph, contemplate how you would estimate the day at which the outbreak peaks. What’s the problem? How would you solve it?
- Rerun the simulation, with the same values you just had. Does anything change? Why (not)?

### Task 2:

- Assume now that half of the infected are asymptomatic. Don’t change any other assumption.
- What do you expect to get for the number/fraction of S/I/R at the end of the outbreak and the time at which the outbreak peaks?
- Run another simulation, record the same values as above.
- Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?

### Task 3:

- Now assume that the asymptomatics transmit at the same rate as the symptomatic (bA= 0.001). Leave everything as in #2.
- How do you expect the results to change? (Try to make as precise/quantitative a prediction as you can)
- Run another simulation, record the same values as above.
- Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?

### Task 4:

- Next, let’s assume that half the symptomatic infected die. Leave everything as in #3.
- How do you expect the results to change?
- Run another simulation, record the same values as above.
- Compare your expectations with the results. How do they agree/disagree? Does it make sense? Anything surprising happening?

### Task 5:

- Further explore how changes in the infectiousness of the different groups (bP, bA, bI) and the average time a person spends in each of those states (gP, gA, gI) affects the infection dynamics.
- Every time, think about what you expect to get, then run the simulation, compare your expectations with the results. Then make sense of it.

### Task 6:

- Further explore how changes in the fraction becoming asymptomatic and fraction dying does (or does not) affect the infection dynamics.
- Every time, think about what you expect to get, then run the simulation, compare your expectations with the results. Then make sense of it.

### Task 7:

- Keep exploring.
- Think about real-world IDs and interventions. What groups would those interventions target, how would that affect the outcome?