## What to do

### Notes

The tasks below are described in a way that assumes everything is in units of MONTHS (rate parameters, therefore, have units of inverse months). If any quantity is not given in those units, you need to convert it first (e.g. if it says a year, you need to convert it to 12 months).

Some of the simulations might take a few seconds to run. Be patient.

### Task 1:

- Set the model parameters such that it corresponds to the following setting:
  - 1000 susceptible hosts and vectors, 1 initially symptomatic host, no infected vectors or pathogen in the environment.
  - Simulation duration approximately 5 years.
  - 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.
  - Assume that there are no births and non-disease deaths occurring.
- 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.

### Task 2:

- Let’s now assume that 50% of infected hosts are asymptomatic and that the duration of the asymptomatic stage is the same as the symptomatic stage.
- 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 11% susceptibles left.
- Assume that we quarantine hosts, and that quarantining reduces infectiousness by half.
- First, we envision a scenario where we can only detect and quarantine individuals that show symptoms. Implement such a scenario, run the simulation and record the number of susceptibles left at the end.
- Next, we envision a scenario where we can quarantine everyone who has become infected, independent of symptom status. Implement such a scenario, again run the simulation and record the number of susceptibles left at the end.

### Task 3:

- Change settings back as they were before you implemented quarantine.
- 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, record the number of susceptibles at the end of the outbreak.
- Now assume that the drug also reduces the duration of the symptomatic period from a month (30 days) to 20 days. Run the simulation, record the number of susceptibles at the end of the outbreak.

### Task 4:

- Of course, intervention strategies are best if they reach all that transmit. Let’s assume now that we still have a drug that targets symptomatic individuals, but that asymptomatic and presymptomatic individuals don’t transmit.
- Set the model such that only symptomatic individuals transmit, at rate 0.004 and duration of the symptomatic period to 1 month. Nobody else transmits. Everything else should be as in task 2. You should get an outbreak of the same size as in task 1.
- Now assume that the drug reduces the duration of the symptomatic period from a month (30 days) to 20 days. Run the simulation, record the number of susceptibles at the end of the outbreak.
- Now assume that the drug also cuts infectiousness by half. Run the simulation, record the number of susceptibles at the end of the outbreak.

### Task 5:

- Set everything as in task 1. Then turn on environmental shedding by symptomatic individuals and decay both at rates of 1 per month. Run an outbreak, record the number of susceptibles at the end.

### Task 6:

- Set rate of transmission from the environment to susceptible hosts to 0.002. Run the simulation.
- Turn off the rate of direct transmission between hosts. Make sure you still get an outbreak.
- Let’s assume different interventions that affect the environmental transmission.
- First, we consider an intervention that leads to a 50% increased pathogen clearance from the environment. Implement that and run the simulation.
- Instead of faster clearance, assume a 50% reduced rate of infection from the environment. Implement that and run the simulation.

### Task 7:

- We’ll now switch to vector-borne transmission. Set everything as in task 1, introduce 1 infected vector.
- Assume that transmission between hosts does not occur. Set transmission from host to vector and vector to host to 0.001.
- Run the simulation. Observe the dynamics of the vectors.
- Now allow for vector births and deaths. Assume that vectors (say mosquitoes) live for half a month. Set birth rate such that vector population balances at 1000. Run the simulation.
- Double host-vector and vector-host transmission rates. Now consider some vector control measures.
- Assume we sprayed against mosquitoes and it reduced the population size by 90%. Set the initial vector population to that value, run the outbreak, observe.

### Task 8:

- Instead of killing vector populations, we now consider a reduction in transmission, e.g. due to the use of bednets.
- Set everything as in task 7 (minus the intervention). Assume that an intervention reduces transmission to vectors by half. Run the simulation, observe.
- Now assume that an intervention reduces transmission from vectors by half. Run the simulation, observe.
- Finally, assume that the intervention reduces transmission both to and from vectors by half. Run the simulation, observe.

### Task 9:

- Keep exploring. The model has many more parameters that you can change, e.g. allowing births and deaths and waning immunity for hosts, or ID that transmit through multiple routes at the same time (e.g. Zika virus). Investigate how different control strategies work under different scenarios.