Primary models
The models we think are most helpful to real-world modeling efforts.
Centroids
The centroids MM describes a basic commuter movement where a fixed proportion of the population commutes every day, travels to another location for 1/3 of a day (with a location likelihood that decreases with distance), and then returns home for the remaining 2/3 of the day.
- tau steps:
(0.333, 0.667) - clauses: CentroidsClause
Flat
This model evenly weights the probability of movement to all other nodes. It uses parameter ‘commuter_proportion’ to determine how many people should be moving, based on the total normal population of each node.
- tau steps:
(0.333, 0.667) - clauses: FlatClause
No
No movement at all. This is handy for cases when you want to disable movement in an experiment, or for testing.
- tau steps:
(1,) - clauses: NoClause
Pei
Modeled after the Pei influenza paper, this model simulates two types of movers – regular commuters and more-randomized dispersers. Each is somewhat stochastic but adhere to the general shape dictated by the commuters array. Both kinds of movement can be “tuned” through their respective parameters: move_control and theta.
- tau steps:
(0.333, 0.667) - clauses: Commuters, Dispersers
Sparsemod
Modeled after the SPARSEMOD COVID-19 paper, this model simulates movement using a distance kernel parameterized by phi, and using a commuters matrix to determine the total expected number of commuters.
- tau steps:
(0.333, 0.667) - clauses: SparsemodClause