Simulate an intervention policy¶

You can simulate an intervention policy to assess the impact of your intervention on a variable of interest. For example, you can see how increasing vaccination rate affects hospitalizations.

Build an intervention policy simulation workflow¶

You can build a workflow to simulate an intervention policy manually.

Before you get started, you'll need a:

Once you've set up and run the workflow, you'll have a set of intervention policy simulation results, which you can use as a dataset.

Build an intervention policy simulation workflow

Add your model to a workflow, hover over its output, and click Link > Configure model. Click Open on the Configure model operator to select or create a new configuration.
Hover over the Model output again and click Link > Create intervention policy. Click Open on the Create intervention policy operator to select or create a new intervention policy.
Hover over the Create intervention policy output and click Link > Simulate.
Click the output of the Configure model operator and connect it to the Simulate model configuration input.
Connect the model configuration output to the simulate input.

Run a simulation¶

Once you've built your workflow, you can edit the Simulate run and output settings to generate the intervention policy simulation.

Run an intervention policy simulation

Click Open on the Simulate operator.
Select the Simulate run settings.
Click Run to start the simulation.
Preview the intervention policy results. To add variables or parameters, use the Output settings.

View and save simulation results with interventions¶

Simulation results are summarized on the operator in the workflow and in the operator details. When you simulate with an intervention you can see the impacts of the intervention. There are visual representations of:

The parameter you changed in your intervention.

At day 2, vaccination rate increases from 10,000 people per day to 25,000 people per day.
Simulation results with the intervention shown in green and results for the baseline (no intervention) in gray.

Increasing vaccination rate from 10,000 people per day to 25,000 people per day decreases hospitalization rate from ~13,000 to ~10,000.

Save the simulation results as a new dataset

On the Output panel, click Save for re-use.

Tip

Using a descriptive naming convention for your datasets will help you keep track of them when you want to compare multiple scenarios.

Troubleshooting¶

If the simulation fails and shows an AssertionError: underflow in dt 0.0 error, the configuration has made the model unsolvable with the selected solver Method. This often happens with the dopri5 solver method.

Workaround: Try using a different solver method, such as rk4 or euler. These solvers are be less efficient than dopri5, but they are also less likely to get caught in an unworkable state