tlo.methods.malaria module

this is the malaria module which assigns malaria infections to the population: asymptomatic, clinical and severe it also holds the hsi events pertaining to malaria testing and treatment including the malaria RDT using DxTest

class Malaria(name=None, resourcefilepath=None)

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

PARAMETERS:

Item	Type	Description
interv	REAL	data frame of intervention coverage by year
clin_inc	REAL	data frame of clinical incidence by age, district, intervention coverage
inf_inc	REAL	data frame of infection incidence by age, district, intervention coverage
sev_inc	REAL	data frame of severe case incidence by age, district, intervention coverage
itn_district	REAL	data frame of ITN usage rates by district
irs_district	REAL	data frame of IRS usage rates by district
sev_symp_prob	REAL	probabilities of each symptom for severe malaria cases
sensitivity_rdt	REAL	Sensitivity of rdt
cfr	REAL	case-fatality rate for severe malaria
dur_asym	REAL	duration (days) of asymptomatic malaria
dur_clin	REAL	duration (days) of clinical symptoms of malaria
dur_clin_para	REAL	duration (days) of parasitaemia for clinical malaria cases
treatment_adjustment	REAL	probability of death from severe malaria if on treatment
p_sev_anaemia_preg	REAL	probability of severe anaemia in pregnant women with clinical malaria
itn_proj	REAL	coverage of ITN for projections 2020 onwards
mortality_adjust	REAL	adjustment of case-fatality rate to match WHO/MAP
data_end	REAL	final year of ICL malaria model outputs, after 2018 = projections
irs_rates_boundary	REAL	threshold for indoor residual spraying coverage
irs_rates_upper	REAL	indoor residual spraying high coverage
irs_rates_lower	REAL	indoor residual spraying low coverage
prob_malaria_case_tests	REAL	probability that a malaria case will have a scheduled rdt
itn	REAL	projected future itn coverage
rdt_testing_rates	REAL	per capita rdt testing rate of general population
scaling_factor_for_rdt_availability	REAL	scaling factor applied to the reports of rdt usage to compensate fornon-availability of rdts at some facilities
duration_iptp_protection_weeks	REAL	duration of protection against clinical malaria conferred by each dose of IPTp
rr_clinical_malaria_hiv_under5	REAL	relative risk of clinical malaria if HIV+ and aged under 5 years
rr_clinical_malaria_hiv_over5	REAL	relative risk of clinical malaria if HIV+ and aged over 5 years
rr_clinical_malaria_hiv_pregnant	REAL	relative risk of clinical malaria if HIV+ and pregnant
rr_clinical_malaria_cotrimoxazole	REAL	relative risk of clinical malaria if on cotrimoxazole
rr_clinical_malaria_art	REAL	relative risk of clinical malaria if HIV+ and on ART and virally suppressed
rr_clinical_malaria_iptp	REAL	relative risk of clinical malaria with each dose of IPTp
rr_severe_malaria_hiv_under5	REAL	relative risk of severe malaria if HIV+ and aged under 5 years
rr_severe_malaria_hiv_over5	REAL	relative risk of severe malaria if HIV+ and aged over 5 years
rr_severe_malaria_hiv_pregnant	REAL	relative risk of clinical malaria if HIV+ and pregnant
rr_severe_malaria_iptp	REAL	relative risk of severe malaria with each dose of IPTp
prob_of_treatment_success	REAL	probability that treatment will clear malaria symptoms
type_of_scaleup	STRING	argument to determine type scale-up of program which will be implemented, can be ‘none’, ‘target’ or ‘max’
scaleup_start_year	INT	the year when the scale-up starts (it will occur on 1st January of that year)
scaleup_parameters	DATA_FRAME	the parameters and values changed in scenario analysis

PROPERTIES:

Item	Type	Description
ma_is_infected	BOOL	Current status of malaria, infected with malaria parasitaemia
ma_date_infected	DATE	Date of latest infection
ma_date_symptoms	DATE	Date of symptom start for clinical infection
ma_date_death	DATE	Date of death due to malaria
ma_tx	CATEGORICAL	Type of anti-malarial treatment person is currently using. Possible values are: [none, uncomplicated, complicated, ]
ma_date_tx	DATE	Date treatment started for most recent malaria episode
ma_inf_type	CATEGORICAL	specific symptoms with malaria infection. Possible values are: [none, asym, clinical, severe, ]
ma_age_edited	REAL	age values redefined to match with malaria data
ma_clinical_counter	INT	annual counter for malaria clinical episodes
ma_dx_counter	INT	annual counter for malaria diagnoses
ma_tx_counter	INT	annual counter for malaria treatment episodes
ma_clinical_preg_counter	INT	annual counter for malaria clinical episodes in pregnant women
ma_iptp	BOOL	if woman has IPTp in current pregnancy

Class attributes:

CAUSES_OF_DEATH : {‘Malaria’: <tlo.methods.causes.Cause object at 0x12a42ce50>}

CAUSES_OF_DISABILITY : {‘Malaria’: <tlo.methods.causes.Cause object at 0x12a42c1d0>}

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

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

OPTIONAL_INIT_DEPENDENCIES : {‘HealthBurden’, ‘Hiv’}

Functions (defined or overridden in class Malaria):

__init__(name=None, resourcefilepath=None)

Create instance of Malaria module

Parameters:

• name – Name of this module (optional, defaults to name of class)

• resourcefilepath – Path to the TLOmodel resources directory

read_parameters(data_folder)

Read parameter values from file, if required.

Must be implemented by subclasses.

Parameters:

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

pre_initialise_population()

Do things required before the population is created * Build the LinearModels

_build_linear_models()

Establish the Linear Models

if HIV is registered, the conditional predictors will apply otherwise only IPTp will affect risk of clinical/severe malaria

initialise_population(population)

Set our property values for the initial population.

Must be implemented by subclasses.

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 its PROPERTIES dictionary.

TODO: We probably need to declare somehow which properties we ‘read’ here, so the simulation knows what order to initialise modules in!

Parameters:

population – the population of individuals

malaria_poll2(population)

general_population_rdt_scheduler(population)

schedule rdt for general population - performed in the community by DCSAs independent of any current symptoms rates are set to match rdt usage reports from WHO / NMCP

initialise_simulation(sim)

• 1. Schedule the Main Regular Polling Events

• 2. Define the DxTests

• 3. Look-up and save the codes for consumables

update_parameters_for_program_scaleup()

options for program scale-up are ‘target’ or ‘max’

on_birth(mother_id, child_id)

Initialise our properties for a newborn individual.

Must be implemented by subclasses.

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

Parameters:

• mother_id – the person id for the mother of this child (can be -1 if the mother is not identified).

• child_id – the person id of new child

report_daly_values()

check_if_fever_is_caused_by_malaria(true_malaria_infection_type: str, diagnosis_function: DiagnosisFunction, person_id: int | None = None, fever_is_a_symptom: bool | None = True, patient_age: int | float | None = None, facility_level: str | None = None, treatment_id: str | None = None) → Literal['severe_malaria', 'clinical_malaria', 'negative_malaria_test']

Run by an HSI when an adult presents with fever. Determine if the cause is malaria.

Optional arguments are used by the logger, and are not needed in the diagnosis.

do_at_generic_first_appt(person_id: int, individual_properties: IndividualProperties, symptoms: List[str], schedule_hsi_event: HSIEventScheduler, diagnosis_function: DiagnosisFunction, facility_level: str, treatment_id: str, **kwargs) → None

Actions to take during a non-emergency generic health system interaction (HSI).

Derived classes should overwrite this method so that they are compatible with the HealthSystem module, and can schedule HSI events when a individual 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 subclass implementing this method using the schedule_hsi_event argument.

Implementations of this method should not make any updates to the population dataframe directly - if the target individuals properties need to be updated this should be performed by updating the individual_properties argument.

Parameters:

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

• individual_properties – Properties of individual target as provided in the population dataframe. Updates to individual properties may be written to this object.

• symptoms – List of symptoms the patient is experiencing.

• schedule_hsi_event – A function that can schedule subsequent HSI events.

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

• consumables_checker – A function that can query the health system 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.

do_at_generic_first_appt_emergency(person_id: int, individual_properties: IndividualProperties, symptoms: List[str], schedule_hsi_event: HSIEventScheduler, diagnosis_function: DiagnosisFunction, facility_level: str, treatment_id: str, **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 MalariaPollingEventDistrict(module)

this calls functions to assign new malaria infections and schedules rdt at a community level (non-symptom driven)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaPollingEventDistrict):

__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 MalariaScaleUpEvent(module)

This event exists to change parameters or functions depending on the scenario for projections which has been set It only occurs once on date: scaleup_start_date, called by initialise_simulation

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaScaleUpEvent):

__init__(module)

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(population)

Apply this event to the given target.

Must be implemented by subclasses.

Parameters:

target – the target of the event

class MalariaIPTp(module)

malaria prophylaxis for pregnant women

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaIPTp):

__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 MalariaEndIPTpProtection(module, person_id)

This resets the properties of a person on IPTp the protective effects ends after 6 weeks and so the property is reset to prevent the malaria poll assuming that this person still has reduced susceptibility to malaria infection

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

Functions (defined or overridden in class MalariaEndIPTpProtection):

__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 MalariaDeathEvent(module, person_id, cause)

Performs the Death operation on an individual and logs it.

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

Functions (defined or overridden in class MalariaDeathEvent):

__init__(module, person_id, cause)

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_Malaria_rdt(module, person_id, facility_level='1a')

this is a point-of-care malaria rapid diagnostic test, with results within 2 minutes default facility level is 1a unless specified

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Malaria_rdt):

__init__(module, person_id, facility_level='1a')

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_Malaria_rdt_community(module, person_id)

this is a point-of-care malaria rapid diagnostic test, with results within 2 minutes this is performed in the community at facility level 0 by a DCSA positive result will schedule a referral to HSI_Malaria_rdt at facility level 1a where a confirmatory rdt will be performed and treatment will be scheduled

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Malaria_rdt_community):

__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_Malaria_Treatment(module, person_id)

this is anti-malarial treatment for all ages. Includes treatment plus one rdt

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Malaria_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.

get_drugs(age_of_person)

Parameters:

age_of_person

Returns:

Helper function to get treatment according to the age of the person being treated. Returns bool to indicate whether drugs were available

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 HSI_Malaria_Treatment_Complicated(module, person_id)

this is anti-malarial treatment for complicated malaria in all ages

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_Malaria_Treatment_Complicated):

__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 HSI_MalariaIPTp(module, person_id)

this is IPTp for pregnant women

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class HSI_MalariaIPTp):

__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 MalariaUpdateEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaUpdateEvent):

__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)

this is a regular event for clinical and severe cases which: * assigns symptoms * schedules rdt * cures people currently on treatment for malaria * clears symptoms for those not on treatment but self-cured * clears parasites if treated

class MalariaParasiteClearanceEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaParasiteClearanceEvent):

__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 MalariaLoggingEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaLoggingEvent):

__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 MalariaTxLoggingEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaTxLoggingEvent):

__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 MalariaPrevDistrictLoggingEvent(module)

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

Class attributes:

__annotations__ : {}

Functions (defined or overridden in class MalariaPrevDistrictLoggingEvent):

__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