Dynamics#
- raphtory.algorithms.temporal_SEIR(graph, seeds, infection_prob, initial_infection, recovery_rate=None, incubation_rate=None, rng_seed=None)#
Simulate an SEIR dynamic on the network
The algorithm uses the event-based sampling strategy from https://doi.org/10.1371/journal.pone.0246961
- Parameters:
graph (GraphView) – the graph view
seeds (int | float | list[Node]) – the seeding strategy to use for the initial infection (if int, choose fixed number of nodes at random, if float infect each node with this probability, if [Node] initially infect the specified nodes
infection_prob (float) – the probability for a contact between infected and susceptible nodes to lead to a transmission
initial_infection (int | str | DateTime) – the time of the initial infection
recovery_rate (float | None) – optional recovery rate (if None, simulates SEI dynamic where nodes never recover) the actual recovery time is sampled from an exponential distribution with this rate
incubation_rate (float | None) – optional incubation rate (if None, simulates SI or SIR dynamics where infected nodes are infectious at the next time step) the actual incubation time is sampled from an exponential distribution with this rate
rng_seed (int | None) – optional seed for the random number generator
- Returns:
Returns an Infected object for each infected node with attributes
infected: the time stamp of the infection event
active: the time stamp at which the node actively starts spreading the infection (i.e., the end of the incubation period)
recovered: the time stamp at which the node recovered (i.e., stopped spreading the infection)
- Return type:
AlgorithmResult[Infected]