_tacoma.eSIS¶

class
_tacoma.
eSIS
¶ Base class for the simulation of an \(\varepsilon\)SIS compartmental infection model on a temporal network. Pass this to
tacoma.api.gillespie_epidemics()
to simulate and retrieve the simulation results.
__init__
(self: _tacoma.eSIS, N: int, t_simulation: float, infection_rate: float, recovery_rate: float, self_infection_rate: float, number_of_initially_infected: int = 1, number_of_initially_vaccinated: int = 0, prevent_disease_extinction: bool = False, sampling_dt: float = 0.0, 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).
 self_infection_rate (float) – Infection rate per susecptible (expected number of reaction events \(S\rightarrow I\) for a single susceptible node per dimension of time).
 number_of_initially_infected (int, default = 1) – Number of nodes which will be in the infected compartment at \(t = t_0\). 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 \(t = t_0\).
 prevent_disease_extinction (bool, default = False) – If this is True, the recovery of the last infected node will always be prevented.
 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.
 verbose (bool, default = False) – Be talkative.
Methods
__init__
(self, N, t_simulation, …)param N: Number of nodes in the temporal network. Attributes
I
A list containing the number of infected at time \(t\). R0
A list containing the basic reproduction number defined as \(R_0(t) = \eta\left\langle k \right\rangle(t) / \rho\) where \(\eta\) is the infection rate per link and \(\rho\) is the recovery rate per node. SI
A list containing the number of \(SI\)links at time \(t\). t_simulation
Absolute run time of the simulation. time
A list containing the time points at which one or more of the observables changed. 