hypercontagion.sim.epidemics

Classic epidemiological models extended to higher-order contagion.

Functions

hypercontagion.sim.epidemics.discrete_SIR(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, initial_recovereds=None, recovery_weight=None, transmission_weight=None, rho=None, tmin=0, tmax=inf, dt=1.0, return_event_data=False, seed=None, **args)

Simulates the discrete SIR model for hypergraphs.

Parameters

• H (xgi.Hypergraph) – The hypergraph on which to simulate the SIR contagion process

• tau (dict) – Keys are edge sizes and values are transmission rates

• gamma (float) – Healing rate

• transmission_function (lambda function, default: threshold) – The contagion function that determines whether transmission is possible.

• initial_infecteds (iterable, default: None) – Initially infected node IDs.

• initial_recovereds (iterable, default: None) – Initially recovered node IDs.

• recovery_weight (hashable, default: None) – Hypergraph node attribute that weights the healing rate.

• transmission_weight (hashable, default: None) – Hypergraph edge attribute that weights the transmission rate.

• rho (float, default: None) – Fraction initially infected. Cannot be specified if initial_infecteds is defined.

• tmin (float, default: 0) – Time at which the simulation starts.

• tmax (float, default: float("Inf")) – Time at which the simulation terminates if there are still infected nodes.

• dt (float, default: 1.0) – The time step of the simulation.

• return_event_data (bool, default: False) – Whether to track each individual transition event that occurs.

• seed (integer, random_state, or None (default)) – Indicator of random number generation state.

Returns

t, S, I, R

Return type

tuple of np.arrays

Raises

HyperContagionError – If the user specifies both rho and initial_infecteds.

hypercontagion.sim.epidemics.discrete_SIS(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, recovery_weight=None, transmission_weight=None, rho=None, tmin=0, tmax=inf, dt=1.0, return_event_data=False, seed=None, **args)

Simulates the discrete SIS model for hypergraphs.

Parameters

• H (xgi.Hypergraph) – The hypergraph on which to simulate the SIR contagion process

• tau (dict) – Keys are edge sizes and values are transmission rates

• gamma (float) – Healing rate

• transmission_function (lambda function, default: threshold) – The contagion function that determines whether transmission is possible.

• initial_infecteds (iterable, default: None) – Initially infected node IDs.

• initial_recovereds (iterable, default: None) – Initially recovered node IDs.

• recovery_weight (hashable, default: None) – Hypergraph node attribute that weights the healing rate.

• transmission_weight (hashable, default: None) – Hypergraph edge attribute that weights the transmission rate.

• rho (float, default: None) – Fraction initially infected. Cannot be specified if initial_infecteds is defined.

• tmin (float, default: 0) – Time at which the simulation starts.

• tmax (float, default: float("Inf")) – Time at which the simulation terminates if there are still infected nodes.

• dt (float, default: 1.0) – The time step of the simulation.

• return_event_data (bool, default: False) – Whether to track each individual transition event that occurs.

• seed (integer, random_state, or None (default)) – Indicator of random number generation state.

Returns

t, S, I

Return type

tuple of np.arrays

Raises

HyperContagionError – If the user specifies both rho and initial_infecteds.

hypercontagion.sim.epidemics.Gillespie_SIR(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=inf, recovery_weight=None, transmission_weight=None, return_event_data=False, seed=None, **args)

Simulates the SIR model for hypergraphs with the Gillespie algorithm.

Parameters

• H (xgi.Hypergraph) – The hypergraph on which to simulate the SIR contagion process

• tau (dict) – Keys are edge sizes and values are transmission rates

• gamma (float) – Healing rate

• transmission_function (lambda function, default: threshold) – The contagion function that determines whether transmission is possible.

• initial_infecteds (iterable, default: None) – Initially infected node IDs.

• initial_recovereds (iterable, default: None) – Initially recovered node IDs.

• recovery_weight (hashable, default: None) – Hypergraph node attribute that weights the healing rate.

• transmission_weight (hashable, default: None) – Hypergraph edge attribute that weights the transmission rate.

• rho (float, default: None) – Fraction initially infected. Cannot be specified if initial_infecteds is defined.

• tmin (float, default: 0) – Time at which the simulation starts.

• tmax (float, default: float("Inf")) – Time at which the simulation terminates if there are still infected nodes.

• return_event_data (bool, default: False) – Whether to track each individual transition event that occurs.

• seed (integer, random_state, or None (default)) – Indicator of random number generation state.

Returns

t, S, I, R

Return type

tuple of np.arrays

Raises

HyperContagionError – If the user specifies both rho and initial_infecteds.

hypercontagion.sim.epidemics.Gillespie_SIS(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, rho=None, tmin=0, tmax=100, recovery_weight=None, transmission_weight=None, return_event_data=False, seed=None, **args)

Simulates the SIS model for hypergraphs with the Gillespie algorithm.

Parameters

• H (xgi.Hypergraph) – The hypergraph on which to simulate the SIR contagion process

• tau (dict) – Keys are edge sizes and values are transmission rates

• gamma (float) – Healing rate

• transmission_function (lambda function, default: threshold) – The contagion function that determines whether transmission is possible.

• initial_infecteds (iterable, default: None) – Initially infected node IDs.

• initial_recovereds (iterable, default: None) – Initially recovered node IDs.

• recovery_weight (hashable, default: None) – Hypergraph node attribute that weights the healing rate.

• transmission_weight (hashable, default: None) – Hypergraph edge attribute that weights the transmission rate.

• rho (float, default: None) – Fraction initially infected. Cannot be specified if initial_infecteds is defined.

• tmin (float, default: 0) – Time at which the simulation starts.

• tmax (float, default: float("Inf")) – Time at which the simulation terminates if there are still infected nodes.

• return_event_data (bool, default: False) – Whether to track each individual transition event that occurs.

• seed (integer, random_state, or None (default)) – Indicator of random number generation state.

Returns

t, S, I

Return type

tuple of np.arrays

Raises

HyperContagionError – If the user specifies both rho and initial_infecteds.

hypercontagion.sim.epidemics.event_driven_SIR(H, tau, gamma, transmission_function=<function majority_vote>, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=inf, return_event_data=False, seed=None, **args)

Simulates the SIR model for hypergraphs with the event-driven algorithm.

Parameters

• H (xgi.Hypergraph) – The hypergraph on which to simulate the SIR contagion process

• tau (dict) – Keys are edge sizes and values are transmission rates

• gamma (float) – Healing rate

• transmission_function (lambda function, default: threshold) – The contagion function that determines whether transmission is possible.

• initial_infecteds (iterable, default: None) – Initially infected node IDs.

• initial_recovereds (iterable, default: None) – Initially recovered node IDs.

• recovery_weight (hashable, default: None) – Hypergraph node attribute that weights the healing rate.

• transmission_weight (hashable, default: None) – Hypergraph edge attribute that weights the transmission rate.

• rho (float, default: None) – Fraction initially infected. Cannot be specified if initial_infecteds is defined.

• tmin (float, default: 0) – Time at which the simulation starts.

• tmax (float, default: float("Inf")) – Time at which the simulation terminates if there are still infected nodes.

• return_event_data (bool, default: False) – Whether to track each individual transition event that occurs.

Returns

t, S, I, R

Return type

tuple of np.arrays

Raises

HyperContagionError – If the user specifies both rho and initial_infecteds.

hypercontagion.sim.epidemics.event_driven_SIS(H, tau, gamma, transmission_function=<function majority_vote>, initial_infecteds=None, rho=None, tmin=0, tmax=inf, return_event_data=False, seed=None, **args)

Simulates the SIS model for hypergraphs with the event-driven algorithm.

Parameters

• H (xgi.Hypergraph) – The hypergraph on which to simulate the SIR contagion process

• tau (dict) – Keys are edge sizes and values are transmission rates

• gamma (float) – Healing rate

• transmission_function (lambda function, default: threshold) – The contagion function that determines whether transmission is possible.

• initial_infecteds (iterable, default: None) – Initially infected node IDs.

• initial_recovereds (iterable, default: None) – Initially recovered node IDs.

• recovery_weight (hashable, default: None) – Hypergraph node attribute that weights the healing rate.

• transmission_weight (hashable, default: None) – Hypergraph edge attribute that weights the transmission rate.

• rho (float, default: None) – Fraction initially infected. Cannot be specified if initial_infecteds is defined.

• tmin (float, default: 0) – Time at which the simulation starts.

• tmax (float, default: float("Inf")) – Time at which the simulation terminates if there are still infected nodes.

• return_event_data (bool, default: False) – Whether to track each individual transition event that occurs.

Returns

t, S, I

Return type

tuple of np.arrays

Raises

HyperContagionError – If the user specifies both rho and initial_infecteds.