Modelling the impact of policies on populations#

PAM uses policies to model change to a population. For example, based on social distancing requirements we might want to reflect that people are expected to make less shared shopping trips or tours. We can do this using the following policy, if you want to define the policy from first principles:

policy_reduce_shopping_activities = HouseholdPolicy(
        ReduceSharedActivity(['shop', 'shop_food']),
        ActivityProbability(['shop', 'shop_food'], 1)
)

There exists a convenience class for this policy and an equivalent policy can be defined in the following way:

policy_reduce_shopping_activities = ReduceSharedHouseholdActivities(
        ['shop', 'shop_food'],
        ActivityProbability(['shop', 'shop_food'], 1)
)

This policy removes all but one the household's shared shopping tours:

PAM-policy-example

In general, a policy is defined in the following way:

You first select the level at which is should be applied:
You then select the modifier, which performs the actions to a person's activities
Finally, you give a likelihood value with which the policy should be applied with. You have a few choices here:
- a number greater than 0 and less or equal to 1. This will be understood to be at the level at which the policy is applied. E.g. PersonPolicy(RemoveActivity(['work']), 0.5) will give each person a fifty-fifty chance of having their work activities removed.
- you can explicitly define at which level a number greater than 0 and less or equal to 1 will be applied. E.g. HouseholdPolicy(RemoveActivity(['work']), PersonProbability(0.5)) will apply a probability of 0.5 per person in a household, and apply the policy to all persons within a household if selected.
- you can also pass a function that operates on a Household, Person or Activity object and returns a number between 0 and 1. E.g. if our function is:
```
def sampler(person):
    if person.attributes['key_worker'] == True:
        return 0
    else:
        return 1
```
  we can define PersonPolicy(RemoveActivity(['work']), PersonProbability(sampler)) which will remove all work activities from anyone who is not a 'key_worker'
- you can choose from:
  - HouseholdProbability
  - PersonProbability
  - ActivityProbability

PAM allows multiple of such policies to be combined to build realistic and complex scenarios. Leveraging activity plans means that PAM can implement detailed policies that are dependent on:

person attributes
household attributes
activity types
travel modes
times
spatial locations
sequences such as tours
any combination of the above

A full overview of policies and examples of the policies available are detailed in this example.

Example modifiers/policies:#

Ill and self-quarantined#

Person quarantine based on age
Household quarantine based on household members

Education activities#

Remove or reduce education based tours/trips including escorts:

Remove education activities based on age
Maintain education for 'care-constrained' households (critical workers)

Work activities#

Furlough and unemployment based on sector
Work from home based on sector
Increase or reduce activities and activity durations based on sector

Shopping activities#

Remove or reduce discretionary shopping
Reduce food shopping
Remove shared tours
Move tours closer to home

Discretionary activities#

Remove or reduce discretionary activities such as leisure
Move tours closer to home
Select the location of discretionary activities, in relation to "anchor"/"mandatory" activities

In-progress modifiers/policies#

Logic also be added to apply:

mode shift
location shift
times
durations