epidemik package

Submodules

epidemik.EpiModel module

class epidemik.EpiModel.EpiModel(compartments=None, seed=None, rng=None)[source]

Bases: object

Simple Epidemic Model Implementation

Provides a way to implement and numerically integrate

R0()[source]

Calculate the basic reproductive ratio (R0) for the model.

Uses the Next-Generation matrix approach defined in J. R. Soc Interface 7, 873 (2010).

Returns:: The value of the largest eigenvalue of the next generation matrix, or None if calculation fails
Return type:: float or None

add_age_structure(matrix, population)[source]

Add age structure to the model using a contact matrix.

Parameters:

matrix (list[list[float]]) – Contact matrix between age groups
population (list[int]) – Population size for each age group

Returns:

The modified model structure

Return type:

list[list]

add_birth_rate(comps=None, rate=None, fixed=False, global_rate=True, **rates)[source]

Add a birth rate to one or more compartments.

Parameters:

rate (float) – Birth rate
comps (list[str], optional) – List of compartments to which to assign this birth rate. If None, apply to all compartments

Return type:

None

Returns:

None

add_death_rate(comps=None, rate=None, fixed=False, global_rate=False, **rates)[source]

Add a death rate to one or more compartments.

Parameters:

rate (float) – Death rate
comps (list[str], optional) – List of compartments to which to assign this death rate. If None, apply to all compartments

Return type:

None

Returns:

None

add_interaction(source, target, agent, rate=None, norm=True, **rates)[source]

Add an interaction between two compartments.

This method adds a directed edge from the source compartment to the target compartment in the transition graph, with the specified agent and rate. The rates are passed as keyword arguments and will be added to the model’s parameters.

Parameters:

source (str) – Name of the source compartment
target (str) – Name of the target compartment
agent (str) – Name of the agent
rates (dict) – Named parameters representing the interaction rates

Return type:

None

Returns:

None

add_spontaneous(source, target, rate=None, **rates)[source]

Add a spontaneous transition between two compartments.

Parameters:

source (str) – Name of the source compartment
target (str) – Name of the target compartment
rates (dict) – Named parameters representing the transition rates

Return type:

None

Returns:

None

add_vaccination(source, target, start, rate, **rates)[source]

Add a vaccination transition between two compartments.

Parameters:

source (str) – Name of the source compartment
target (str) – Name of the target compartment
rate (float) – Rate of the vaccination
start (int) – Start time of the vaccination

Return type:

None

Returns:

None

add_viral_generation(source, target, source_rate=None, target_rate=None, **rates)[source]

Add a viral generation transition

Return type:: None

Parameters: - source: string

Name of the source compartment

target: string
Name of the target compartment
source_rate: float
Rate of destruction of infected cells
target_rate: float
Rate of creation of viral particles

download_model(repo=None, load_model=True)[source]

Download model from offical repository

Return type:: Optional[Self]

draw_model(ax=None, show=True)[source]

Plot the model structure.

Parameters:

ax (matplotlib.axes._subplots.AxesSubplot, optional) – The Axes object to plot to. If None, a new figure is created
show (bool, default=True) – Whether to call plt.show() or not

Return type:

None

Returns:

None

integrate(timesteps, t_min=1, seasonality=None, **kwargs)[source]

Numerically integrate the epidemic model.

Parameters:

timesteps (int) – Number of time steps to integrate
t_min (int, default=1) – Starting time
seasonality (numpy.ndarray, optional) – Array of seasonality values
kwargs (dict) – Initial population of each compartment

Return type:

None

Returns:

None

list_models()[source]

List the models available in the official repository

Return type:: List[str]

load_model()[source]

Load a model from a file.

Parameters:: filename (str) – Name of the file to load the model from
Returns:: The loaded model
Return type:: EpiModel

plot(title=None, normed=True, show=True, ax=None, **kwargs)[source]

Convenience function for plotting model results.

Parameters:

title (str, optional) – Title of the plot
normed (bool, default=True) – Whether to normalize the values or not
show (bool, default=True) – Whether to call plt.show() or not
ax (matplotlib.axes._subplots.AxesSubplot, optional) – The Axes object to plot to. If None, a new figure is created
kwargs (dict) – Additional arguments to pass to the plot function

Returns:

The plot object

Return type:

matplotlib.axes._subplots.AxesSubplot

save_model(filename)[source]

Save the model to a file.

Parameters:: filename (str) – Name of the file to save the model to
Return type:: None
Returns:: None

simulate(timesteps, t_min=1, seasonality=None, **kwargs)[source]

Stochastically simulate the epidemic model.

Parameters:

timesteps (int) – Number of time steps to simulate
t_min (int, default=1) – Starting time
seasonality (numpy.ndarray, optional) – Array of seasonal factors
kwargs (dict) – Initial population of each compartment

Return type:

None

Returns:

None

single_step(seasonality=None, **kwargs)[source]

Perform a single simulation step.

Parameters:

seasonality (numpy.ndarray, optional) – Array of seasonality values
kwargs (dict) – Initial population of each compartment if simulation hasn’t started

Returns:

None

epidemik.MetaEpiModel module

class epidemik.MetaEpiModel.MetaEpiModel(travel_graph, populations, population='Population', seed=None)[source]

Bases: object

Simple Epidemic Model Implementation

Provides a way to implement and numerically integrate

R0()[source]

add_interaction(source, target, agent, **rates)[source]

Add an interaction between two compartments_

Return type:: None

Parameters: - source: string

Name of the source compartment

target: string
Name of the target compartment
agent: string
Name of the agent
rate: float
Rate of the interaction

Returns: None

add_spontaneous(source, target, **rates)[source]

Add a spontaneous transition between two compartments_

Parameters: - source: string

Name of the source compartment

target: string
Name of the target compartment
rate: float
Rate of the transition

Returns: None

add_vaccination(source, target, start, **rates)[source]

Add a vaccination transition between two compartments_

Return type:: None

Parameters: - source: string

Name of the source compartment

target: string
Name of the target compartment
rate: float
Rate of the vaccination
start: int
Start time of the vaccination

Returns: None

draw_model()[source]

Return type:: None

get_state(state)[source]

Return a reference to a state EpiModel object

Return type:: EpiModel

Parameters: - state: string

Name of the state to return

integrate(**kwargs)[source]

plot(title=None, normed=True, layout=None, **kwargs)[source]

Return type:: None

plot_peaks()[source]

Return type:: None

simulate(timestamp, t_min=1, seasonality=None, seed_state=None, susceptible='S', **kwargs)[source]

Return type:: None

epidemik.NetworkEpiModel module

class epidemik.NetworkEpiModel.NetworkEpiModel(network, compartments=None)[source]

Bases: EpiModel

R0()[source]

Calculate the basic reproductive ratio (R0) for the model.

Uses the Next-Generation matrix approach defined in J. R. Soc Interface 7, 873 (2010).

Returns:: The value of the largest eigenvalue of the next generation matrix, or None if calculation fails
Return type:: float or None

add_interaction(source, target, agent, rate=None, rescale=False, **rates)[source]

Add an interaction between two compartments.

This method adds a directed edge from the source compartment to the target compartment in the transition graph, with the specified agent and rate. The rates are passed as keyword arguments and will be added to the model’s parameters.

Parameters:

source (str) – Name of the source compartment
target (str) – Name of the target compartment
agent (str) – Name of the agent
rates (dict) – Named parameters representing the interaction rates

Return type:

None

Returns:

None

add_spontaneous(source, target, rate, **rates)[source]

Add a spontaneous transition between two compartments.

Parameters:

source (str) – Name of the source compartment
target (str) – Name of the target compartment
rates (dict) – Named parameters representing the transition rates

Returns:

None

integrate(timesteps, **kwargs)[source]

Numerically integrate the epidemic model.

Parameters:

timesteps (int) – Number of time steps to integrate
t_min (int, default=1) – Starting time
seasonality (numpy.ndarray, optional) – Array of seasonality values
kwargs (dict) – Initial population of each compartment

Returns:

None

simulate(timesteps, seeds, **kwargs)[source]

Stochastically simulate the epidemic model

Return type:: None

epidemik.utils module

exception epidemik.utils.NotImplementedError[source]: Bases: Exception

exception epidemik.utils.NotInitialized[source]: Bases: Exception

class epidemik.utils.Parameters[source]

Bases: dict

compute_parameter(param)[source]

Compute the rate from a string

Return type:: float

Parameters: - rate: string

Rate of the transition

Returns: float

The computed rate

define_parameters(**kwargs)[source]

Define one or more parameter for the model

Return type:: None

Parameters: - kwargs: keyword arguments

Named parameters for the model

Returns: None

epidemik.utils.get_cache_directory()[source]: Return the location of the cache directory for the current platform.

Module contents

epidemik

epidemik is a Python package for the simulation of Compartmental Epidemic Models.

See https://www.github.com/DataForScience/epidemik for complete documentation.