_tacoma.SI

class _tacoma.SI

Base class for the simulation of an SI compartmental infection model on a temporal network. Pass this to tacoma.api.gillespie_SI() to simulate and retrieve the simulation results. Simulation stops when t_simulation is reached or if no infected is left.

__init__(self: _tacoma.SI, N: int, t_simulation: float, infection_rate: float, number_of_initially_infected: int=1, number_of_initially_vaccinated: int=0, sampling_dt: float=0.0, seed: int=0, save_infection_events: bool=False, 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).
  • number_of_initially_infected (int, default = 1) – Number of nodes which will be in the infected compartment at \(t = t_0\).
  • number_of_initially_vaccinated (int, default = 0) – Number of nodes which will be in the recovered compartment at \(t = t_0\).
  • sampling_dt (float, default = 0.0) – If this is >0.0, save observables roughly every sampling_dt instead of on every change.
  • seed (int, default = 0) – Seed for RNG initialization. If this is 0, the seed will be initialized randomly.
  • save_infection_events (bool, default = False) – If true, the edge along which each infection event occurs is saved in the variable infection_events.
  • verbose (bool, default = False) – Be talkative.

Methods

__init__

Attributes

I A list containing the number of infected at time \(t\).
SI A list containing the number of \(SI\)-links at time \(t\).
infection_events A list containing the edges along which each infection event took place, in the form (infection_source, susceptible).
t_simulation Absolute run time of the simulation.
time A list containing the time points at which one or more of the observables changed.