tlo.methods.measles module

class Measles(name=None, resourcefilepath=None)

This module represents measles infections and disease.

Bases: tlo.core.Module

PARAMETERS:

Item	Type	Description
beta_baseline	REAL	Baseline measles transmission probability
beta_scale	REAL	Scale value for measles transmission probability sinusoidal function
phase_shift	REAL	Phase shift for measles transmission probability sinusoidal function
period	REAL	Period for measles transmission probability sinusoidal function
vaccine_efficacy_1	REAL	Efficacy of first measles vaccine dose against measles infection
vaccine_efficacy_2	REAL	Efficacy of second measles vaccine dose against measles infection
prob_severe	REAL	Probability of severe measles infection, requiring hospitalisation
risk_death_on_treatment	REAL	Risk of scheduled death occurring if on treatment for measles complications
symptom_prob	DATA_FRAME	Probability of each symptom with measles infection
case_fatality_rate	DICT	Probability that case of measles will result in death if not treated

PROPERTIES:

Item	Type	Description
me_has_measles	BOOL	Measles infection status
me_date_measles	DATE	Date of onset of measles
me_on_treatment	BOOL	Currently on treatment for measles complications

Class attributes:

CAUSES_OF_DEATH : {‘Measles’: <tlo.methods.causes.Cause object at 0x135ea6390>}

CAUSES_OF_DISABILITY : {‘Measles’: <tlo.methods.causes.Cause object at 0x135ea4350>}

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

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

OPTIONAL_INIT_DEPENDENCIES : {‘HealthBurden’}

Functions (defined or overridden in class Measles):

__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

pre_initialise_population()

Carry out any work before any populations have been initialised

This optional method allows access to all other registered modules, before any of the modules have initialised a population. This is expected to be useful for when a module’s properties rely upon information from other modules.

initialise_population(population)

Set our property values for the initial population. set whole population to measles-free for 1st jan

initialise_simulation(sim)

Schedule measles event to start straight away. Each month it will assign new infections

on_birth(mother_id, child_id)

Initialise our properties for a newborn individual assume all newborns are uninfected

Parameters:

• mother_id – the ID for the mother for this child

• child_id – the ID for the new child

report_daly_values()

process_parameters()

Process the parameters (following being read-in) prior to the simulation starting. Make self.symptom_probs to be a dictionary keyed by age, with values of dictionaries keyed by symptoms and the probability of symptom onset.

do_at_generic_first_appt(patient_id: int, symptoms: List[str], **kwargs) → Dict[str, Any]

Actions to be take during a NON-emergency generic HSI.

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

When overwriting, arguments that are not required can be left out of the definition. If done so, the method MUST take a **kwargs input to avoid errors when looping over all disease modules and running their generic HSI methods.

HSI_Events should be scheduled by the Module implementing this method using the Module.healthsystem.schedule_hsi() method. However, they should not write updates back to the population DataFrame in this method - these values should be returned as a dictionary as described below:

The return value of this function should be a dictionary containing any changes that need to be made to the individual’s row in the population DataFrame. Key/value pairs should be the column name and the new value to assign to the patient. In the event no updates are required; return an object that evaluates to False when cast to a bool. Your options are: - Omit a return statement and value (preferred). - Return an empty dictionary. Use this case when patient details might need updating conditionally, on EG patient symptoms or consumable availability. In which case, an empty dictionary should be created and key-value pairs added to this dictionary as such conditionals are checked. If no conditionals are met, the empty dictionary will be returned. - Use a return statement with no values (use if the logic of your module-specific method necessitates the explicit return). - Return None (not recommended, use “return” on its own, as above).

Parameters:

• patient_id – Row index (ID) of the individual target of the HSI event in the population DataFrame.

• patient_details – Patient details as provided in the population DataFrame.

• symptoms – List of symptoms the patient is experiencing.

• diagnosis_function – A function that can run diagnosis tests based on the patient’s symptoms.

• consumables_checker – A function that can query the HealthSystem to check for available consumables.

• facility_level – The level of the facility that the patient presented at.

• treatment_id – The treatment id of the HSI event triggering the generic appointment.

• random_state – Random number generator to be used when making random choices during event creation.

class MeaslesEvent(module)

MeaslesEvent runs every month and creates a number of new infections which are scattered across the month. * Seasonality is captured by the risk of infection changing according to the month. * Vaccination lowers an individual’s likelihood of getting the infection (one dose will be 85% protective and two

doses will be 99% protective).

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MeaslesEvent):

__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)

Apply this event to the given target.

This is a no-op; subclasses should override this method.

Parameters:

target – the target of the event

class MeaslesOnsetEvent(module, person_id)

Bases: tlo.events.Event, tlo.events.IndividualScopeEventMixin

Functions (defined or overridden in class MeaslesOnsetEvent):

__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.

• priority – a keyword-argument to set the priority (see Priority enum)

apply(person_id)

Onset a case of Measles

assign_symptoms(_age)

Assign symptoms for this case and returns the date on which symptom onset. (Parameter values specify that everybody gets rash, fever and eye complain.)

get_prob_death(_age)

Returns the probability of death for this person based on their age and whether they have untreated HIV.

class MeaslesSymptomResolveEvent(module, person_id)

Bases: tlo.events.Event, tlo.events.IndividualScopeEventMixin

Functions (defined or overridden in class MeaslesSymptomResolveEvent):

__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.

• priority – a keyword-argument to set the priority (see Priority enum)

apply(person_id)

this event is called by MeaslesOnsetEvent and HSI_Measles_Treatment

class MeaslesDeathEvent(module, person_id)

Performs the Death operation on an individual and logs it.

Bases: tlo.events.Event, tlo.events.IndividualScopeEventMixin

Functions (defined or overridden in class MeaslesDeathEvent):

__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.

• priority – a keyword-argument to set the priority (see Priority enum)

apply(person_id)

Apply this event to the given target.

Must be implemented by subclasses.

Parameters:

target – the target of the event

class HSI_Measles_Treatment(module, person_id)

Health System Interaction Event It is the event when a person with diagnosed measles receives treatment

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Measles_Treatment):

__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.

did_not_run()

Called when this event is due but it is not run. Return False to prevent the event being rescheduled, or True to allow the rescheduling. This is called each time that the event is tried to be run but it cannot be.

class MeaslesLoggingEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MeaslesLoggingEvent):

__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)

Log Summary Statistics Monthly

class MeaslesLoggingFortnightEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MeaslesLoggingFortnightEvent):

__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)

Log Summary Statistics Every Two Weeks

class MeaslesLoggingAnnualEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MeaslesLoggingAnnualEvent):

__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)

Log Summary Statistics Annually