tlo.methods.epilepsy module

class Epilepsy(name=None, resourcefilepath=None)

Construct a new disease module ready to be included in a simulation.

Initialises an empty parameters dictionary and module-specific random number generator.

Parameters:

name – the name to use for this module. Defaults to the concrete subclass’ name.

Bases: tlo.core.Module, tlo.methods.hsi_generic_first_appts.GenericFirstAppointmentsMixin

PARAMETERS:

Item	Type	Description
init_epil_seiz_status	LIST	Proportions in each seizure status category at baseline
init_prop_antiepileptic_seiz_stat_1	REAL	initial proportions on antiepileptic by if seizure status = 1
init_prop_antiepileptic_seiz_stat_2	REAL	initial proportions on antiepileptic by if seizure status = 2
init_prop_antiepileptic_seiz_stat_3	REAL	initial proportions on antiepileptic by if seizure status = 3
base_3m_prob_epilepsy	REAL	base probability of epilepsy per 3 month period if age < 20
rr_epilepsy_age_ge20	REAL	relative rate of epilepsy if age over 20
prop_inc_epilepsy_seiz_freq	REAL	proportion of incident epilepsy cases with frequent seizures
base_prob_3m_seiz_stat_freq_infreq	REAL	base probability per 3 months of seizure status frequent if current infrequent
rr_effectiveness_antiepileptics	REAL	relative rate of seizure status frequent if current infrequent if on antiepileptic
base_prob_3m_seiz_stat_infreq_freq	REAL	base probability per 3 months of seizure status infrequent if current frequent
base_prob_3m_seiz_stat_infreq_none	REAL	base probability per 3 months of seizure status infrequent if current nonenow
base_prob_3m_seiz_stat_none_freq	REAL	base probability per 3 months of seizure status nonenow if current frequent
base_prob_3m_seiz_stat_none_infreq	REAL	base probability per 3 months of seizure status nonenow if current infrequent
base_prob_3m_stop_antiepileptic	REAL	base probability per 3 months of stopping antiepileptic, if nonenow seizures
rr_stop_antiepileptic_seiz_infreq_or_freq	REAL	relative rate of stopping antiepileptic if infrequent or frequent seizures
base_prob_3m_epi_death	REAL	base probability per 3 months of epilepsy death
daly_wt_epilepsy_severe	REAL	disability weight for severe epilepsycontrolled phase - code 860
daly_wt_epilepsy_less_severe	REAL	disability weight for less severe epilepsycontrolled phase - code 861
daly_wt_epilepsy_seizure_free	REAL	disability weight for less severe epilepsycontrolled phase - code 862

PROPERTIES:

Item	Type	Description
ep_seiz_stat	CATEGORICAL	(0 = never epilepsy, 1 = previous seizures none now, 2 = infrequent seizures, 3 = frequent seizures). Possible values are: [0, 1, 2, 3, ]
ep_antiep	BOOL	on antiepileptic
ep_epi_death	BOOL	epilepsy death this 3 month period
ep_unified_symptom_code	CATEGORICAL	. Possible values are: [0, 1, 2, 3, ]
ep_disability	REAL	disability weight for current 3 month period

Class attributes:

CAUSES_OF_DEATH : {‘Epilepsy’: <tlo.methods.causes.Cause object at 0x128fba650>}

CAUSES_OF_DISABILITY : {‘Epilepsy’: <tlo.methods.causes.Cause object at 0x128fb84d0>}

INIT_DEPENDENCIES : {‘SymptomManager’, ‘HealthBurden’, ‘Demography’, ‘HealthSystem’}

METADATA : {<Metadata.USES_HEALTHSYSTEM: 3>, <Metadata.USES_SYMPTOMMANAGER: 2>, <Metadata.USES_HEALTHBURDEN: 4>, <Metadata.DISEASE_MODULE: 1>}

TREATMENT_ID : antiepileptic

__annotations__ : {}

Functions (defined or overridden in class Epilepsy):

__init__(name=None, resourcefilepath=None)

Construct a new disease module ready to be included in a simulation.

Initialises an empty parameters dictionary and module-specific random number generator.

Parameters:

name – the name to use for this module. Defaults to the concrete subclass’ name.

read_parameters(data_folder)

Read parameter values from file, if required.

Here we just assign parameter values explicitly.

Parameters:

data_folder – path of a folder supplied to the Simulation containing data files. Typically modules would read a particular file within here.

initialise_population(population)

Set our property values for the initial population.

This method is called by the simulation when creating the initial population, and is responsible for assigning initial values, for every individual, of those properties ‘owned’ by this module, i.e. those declared in the PROPERTIES dictionary above.

initialise_simulation(sim)

Get ready for simulation start.

This method is called just before the main simulation loop begins, and after all modules have read their parameters and the initial population has been created. It is a good place to add initial events to the event queue.

Here we add our three-monthly event to poll the population for epilepsy starting or stopping.

on_birth(mother_id, child_id)

Initialise our properties for a newborn individual.

This is called by the simulation whenever a new person is born.

Parameters:

• mother – the mother for this child

• child – the new child

report_daly_values()

transition_seizure_stat()

This function handles all transitions in epilepsy seizure status, for those on and off anti epileptics. The function determines the current seizure status of those with epilepsy and based on their original status, and whether they are on anti epileptics determines a new seizure status :return:

stop_antiep(indices, probability)

stop individuals on antiep with given probability

get_best_available_medicine(hsi_event) → None | str

Returns the best available medicine (as string), or None if none are available

do_at_generic_first_appt_emergency(person_id: int, symptoms: List[str], schedule_hsi_event: HSIEventScheduler, **kwargs) → None

Actions to take during an emergency generic health system interaction (HSI).

Call signature is identical to the do_at_generic_first_appt() method.

Derived classes should overwrite this method so that they are compatible with the :py:class`~.HealthSystem` module, and can schedule HSI events when a individual presents symptoms indicative of the corresponding illness or condition.

class EpilepsyEvent(module)

The regular event that actually changes individuals’ epilepsy status

Regular events automatically reschedule themselves at a fixed frequency, and thus implement discrete timestep type behaviour. The frequency is specified when calling the base class constructor in our __init__ method.

Create a new depr event.

We need to pass the frequency at which we want to occur to the base class constructor using super(). We also pass the module that created this event, so that random number generators can be scoped per-module.

Parameters:

module – the module that created this event

Bases: tlo.events.RegularEvent, tlo.events.Event, tlo.events.PopulationScopeEventMixin

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class EpilepsyEvent):

__init__(module)

Create a new depr event.

We need to pass the frequency at which we want to occur to the base class constructor using super(). We also pass the module that created this event, so that random number generators can be scoped per-module.

Parameters:

module – the module that created this event

apply(population)

Apply this event to the population.

For efficiency, we use pandas operations to scan the entire population in bulk.

Parameters:

population – the current population

class EpilepsyLoggingEvent(module)

Create a new regular event.

Parameters:

• module – the module that created this event

• frequency (pandas.tseries.offsets.DateOffset) – the interval from one occurrence to the next (must be supplied as a keyword argument)

Bases: tlo.events.RegularEvent, tlo.events.Event, tlo.events.PopulationScopeEventMixin

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class EpilepsyLoggingEvent):

__init__(module)

Create a new regular event.

Parameters:

• module – the module that created this event

• frequency (pandas.tseries.offsets.DateOffset) – the interval from one occurrence to the next (must be supplied as a keyword argument)

apply(population)

Get some summary statistics and log them

class HSI_Epilepsy_Start_Anti_Epileptic(module, person_id)

This is a Health System Interaction Event.

Create a new event.

Note that just creating an event does not schedule it to happen; that must be done by calling Simulation.schedule_event.

Parameters:

module – the module that created this event. All subclasses of Event take this as the first argument in their constructor, but may also take further keyword arguments.

Bases: tlo.methods.hsi_event.HSI_Event, tlo.events.IndividualScopeEventMixin

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Epilepsy_Start_Anti_Epileptic):

__init__(module, person_id)

Create a new event.

Note that just creating an event does not schedule it to happen; that must be done by calling Simulation.schedule_event.

Parameters:

module – the module that created this event. All subclasses of Event take this as the first argument in their constructor, but may also take further keyword arguments.

apply(person_id, squeeze_factor)

Apply this event to the population.

Must be implemented by subclasses.

class HSI_Epilepsy_Follow_Up(module, person_id)

Create a new event.

Note that just creating an event does not schedule it to happen; that must be done by calling Simulation.schedule_event.

Parameters:

module – the module that created this event. All subclasses of Event take this as the first argument in their constructor, but may also take further keyword arguments.

Bases: tlo.methods.hsi_event.HSI_Event, tlo.events.IndividualScopeEventMixin

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Epilepsy_Follow_Up):

__init__(module, person_id)

Create a new event.

Note that just creating an event does not schedule it to happen; that must be done by calling Simulation.schedule_event.

Parameters:

module – the module that created this event. All subclasses of Event take this as the first argument in their constructor, but may also take further keyword arguments.

apply(person_id, squeeze_factor)

Apply this event to the population.

Must be implemented by subclasses.