_tacoma.QS_SIS¶

class
_tacoma.
QS_SIS
¶ Base class for the simulation of an quasistationary SIS compartmental infection model on a temporal network. Pass this to
tacoma.api.quasistationary_simulation()
to simulate and retrieve the simulation results.
__init__
(self: _tacoma.QS_SIS, N: int, t_simulation: float, infection_rate: float, recovery_rate: float, N_QS_samples: int, sampling_rate: float, number_of_initially_infected: int = 1, number_of_initially_vaccinated: int = 0, sample_network_state: bool = True, seed: int = 0, verbose: bool = False) → None¶ Parameters:  N (int) – Number of nodes in the temporal network.
 t_simulation (float) – Maximum time for the simulation to run. Can possibly be greater than the maximum time of the temporal network in which case the temporal network is looped.
 infection_rate (float) – Infection rate per \(SI\)link (expected number of reaction events \(SI\rightarrow II\) for a single \(SI\)link per dimension of time).
 recovery_rate (float) – Recovery rate per infected (expected number of reaction events \(I\rightarrow S\) for a single infected node per dimension of time).
 N_QS_samples (int) – Number of quasistationary configuration samples to be saved during the simulation.
 sampling_rate (float) – Rate with which to sample for the quasistationary configuration collection.
 number_of_initially_infected (int, default = 1) – Number of nodes which will be in the infected compartment at \(t = t_0\). Note that the default value 1 is not an ideal initial value as fluctuations may lead to a quick end of the simulation skewing the outcome. I generally recommend to use a number of the order of \(N/2\).
 number_of_initially_vaccinated (int, default = 0) – Number of nodes which will be in the recovered compartment at \(t = t_0\).
 sample_network_state (bool, default = True) – Do not only sample the node stati but also the current network structure.
 seed (int, default = 0) – Seed for RNG initialization. If this is 0, the seed will be initialized randomly.
 verbose (bool, default = False) – Be talkative.
Methods
__init__
(self, N, t_simulation, …)param N: Number of nodes in the temporal network. ended_in_absorbing_state
(self)Return whether or not the simulation ended in an absorbing state. get_infection_observables
(self)Returns \(\left\langle I \right\rangle\) and get_random_configuration
(self)Get a random configuration from the quasistationary set_initial_configuration
(self, arg0, arg1)Set a time and node statii. Attributes
configurations
Sampled collection. last_active_time
The last time the model was active. t_simulation
. 