hpvsim.interventions module

Specify the core interventions. Other interventions can be defined by the user by inheriting from these classes.

class Intervention(label=None, show_label=False, do_plot=None, line_args=None, **kwargs)

Bases: object

Base class for interventions.

Parameters:

• label (str) – a label for the intervention (used for plotting, and for ease of identification)

• show_label (bool) – whether or not to include the label in the legend

• do_plot (bool) – whether or not to plot the intervention

• line_args (dict) – arguments passed to pl.axvline() when plotting

disp()

Print a detailed representation of the intervention

initialize(sim=None)

Initialize intervention – this is used to make modifications to the intervention that can’t be done until after the sim is created.

finalize(sim=None)

Finalize intervention

This method is run once as part of sim.finalize() enabling the intervention to perform any final operations after the simulation is complete (e.g. rescaling)

apply(sim)

Apply the intervention. This is the core method which each derived intervention class must implement. This method gets called at each timestep and can make arbitrary changes to the Sim object, as well as storing or modifying the state of the intervention.

Parameters:

sim – the Sim instance

Returns:

None

shrink(in_place=False)

Remove any excess stored data from the intervention; for use with sim.shrink().

Parameters:

in_place (bool) – whether to shrink the intervention (else shrink a copy)

plot_intervention(sim, ax=None, **kwargs)

Plot the intervention

This can be used to do things like add vertical lines at timepoints when interventions take place. Can be disabled by setting self.do_plot=False.

Note 1: you can modify the plotting style via the line_args argument when creating the intervention.

Note 2: By default, the intervention is plotted at the timepoints stored in self.timepoints. However, if there is a self.plot_timepoints attribute, this will be used instead.

Parameters:

• sim – the Sim instance

• ax – the axis instance

• kwargs – passed to ax.axvline()

Returns:

None

to_json()

Return JSON-compatible representation

Custom classes can’t be directly represented in JSON. This method is a one-way export to produce a JSON-compatible representation of the intervention. In the first instance, the object dict will be returned. However, if an intervention itself contains non-standard variables as attributes, then its to_json method will need to handle those.

Note that simply printing an intervention will usually return a representation that can be used to recreate it.

Returns:

JSON-serializable representation (typically a dict, but could be anything else)

class RoutineDelivery(years=None, start_year=None, end_year=None, prob=None, annual_prob=True)

Bases: Intervention

Base class for any intervention that uses routine delivery; handles interpolation of input years.

initialize(sim)

class CampaignDelivery(years, interpolate=None, prob=None, annual_prob=True)

Bases: Intervention

Base class for any intervention that uses campaign delivery; handles interpolation of input years.

initialize(sim)

class dynamic_pars(pars=None, **kwargs)

Bases: Intervention

A generic intervention that modifies a set of parameters at specified points in time.

The intervention takes a single argument, pars, which is a dictionary of which parameters to change, with following structure: keys are the parameters to change, then subkeys ‘days’ and ‘vals’ are either a scalar or list of when the change(s) should take effect and what the new value should be, respectively.

You can also pass parameters to change directly as keyword arguments.

Parameters:

• pars (dict) – described above

• kwargs (dict) – passed to Intervention()

Examples:

interv = hpv.dynamic_pars(condoms=dict(timepoints=10, vals={'c':0.9})) # Increase condom use amount casual partners to 90%
interv = hpv.dynamic_pars({'beta':{'timepoints':[10, 15], 'vals':[0.005, 0.015]}, # At timepoint 10, reduce beta, then increase it again
                          'debut':{'timepoints':10, 'vals':dict(f=dict(dist='normal', par1=20, par2=2.1), m=dict(dist='normal', par1=19.6, par2=1.8))}}) # Increase mean age of sexual debut

initialize(sim)

Initialize with a sim

apply(sim)

Loop over the parameters, and then loop over the timepoints, applying them if any are found

class EventSchedule

Bases: Intervention

Run functions on different days

This intervention is a a kind of generalization of dynamic_pars to allow more flexibility in triggering multiple, arbitrary operations and to more easily assemble multiple changes at different times. This intervention can be used to implement scale-up or other changes to interventions without needing to implement time-dependency in the intervention itself.

To use the intervention, simply index the intervention by t or by date.

Example:

>>> iv = EventSchedule()
>>> iv[1] = lambda sim: print(sim.t)
>>> iv['2020-04-02'] = lambda sim: print('foo')

initialize(sim)

apply(sim)

set_intervention_attributes(sim, intervention_name, **kwargs)

class BaseVaccination(product=None, prob=None, age_range=None, sex=None, eligibility=None, label=None, **kwargs)

Bases: Intervention

Base vaccination class for determining who will receive a vaccine.

Parameters:

• product (str/Product) – the vaccine to use

• prob (float/arr) – annual probability of eligible population getting vaccinated

• age_range (list/tuple) – age range to vaccinate

• sex (int/str/list) – sex to vaccinate - accepts 0/1 or ‘f’/’m’ or a list of both

• eligibility (inds/callable) – indices OR callable that returns inds

• label (str) – the name of vaccination strategy

• kwargs (dict) – passed to Intervention()

initialize(sim)

check_eligibility(sim)

Determine who is eligible for vaccination

apply(sim)

Perform vaccination by finding who’s eligible for vaccination, finding who accepts, and applying the vaccine product.

shrink(in_place=True)

Shrink vaccination intervention

class routine_vx(product=None, prob=None, age_range=None, sex=0, eligibility=None, start_year=None, end_year=None, years=None, **kwargs)

Bases: BaseVaccination, RoutineDelivery

Routine vaccination - an instance of base vaccination combined with routine delivery. See base classes for a description of input arguments.

Examples:

vx1 = hpv.routine_vx(product='bivalent', age_range=[9,10], prob=0.9, start_year=2025) # Vaccinate 90% of girls aged 9-10 every year
vx2 = hpv.routine_vx(product='bivalent', age_range=[9,10], prob=0.9, sex=[0,1], years=np.arange(2020,2025)) # Screen 90% of girls and boys aged 9-10 every year from 2020-2025
vx3 = hpv.routine_vx(product='quadrivalent', prob=np.linspace(0.2,0.8,5), years=np.arange(2020,2025)) # Scale up vaccination over 5 years starting in 2020

initialize(sim)

class campaign_vx(product=None, prob=None, age_range=None, sex=0, eligibility=None, years=None, interpolate=True, **kwargs)

Bases: BaseVaccination, CampaignDelivery

Campaign vaccination - an instance of base vaccination combined with campaign delivery. See base classes for a description of input arguments.

initialize(sim)

class BaseTest(product=None, prob=None, eligibility=None, **kwargs)

Bases: Intervention

Base class for screening and triage.

Parameters:

• product (str/Product) – the diagnostic to use

• prob (float/arr) – annual probability of eligible women receiving the diagnostic

• eligibility (inds/callable) – indices OR callable that returns inds

• label (str) – the name of screening strategy

• kwargs (dict) – passed to Intervention()

initialize(sim)

deliver(sim)

Deliver the diagnostics by finding who’s eligible, finding who accepts, and applying the product.

check_eligibility(sim)

class BaseScreening(age_range=None, **kwargs)

Bases: BaseTest

Base class for screening.

Parameters:

• age_range (list/tuple/arr) – age range for screening, e.g. [30,50]

• kwargs (dict) – passed to BaseTest

check_eligibility(sim)

Return an array of indices of agents eligible for screening at time t, i.e. sexually active females in age range, plus any additional user-defined eligibility, which often includes the screening interval.

apply(sim)

Perform screening by finding who’s eligible, finding who accepts, and applying the product.

class routine_screening(product=None, prob=None, eligibility=None, age_range=None, years=None, start_year=None, end_year=None, **kwargs)

Bases: BaseScreening, RoutineDelivery

Routine screening - an instance of base screening combined with routine delivery. See base classes for a description of input arguments.

Examples:

screen1 = hpv.routine_screening(product='hpv', prob=0.02) # Screen 2% of the eligible population every year
screen2 = hpv.routine_screening(product='hpv', prob=0.02, start_year=2020) # Screen 2% every year starting in 2020
screen3 = hpv.routine_screening(product='hpv', prob=np.linspace(0.005,0.025,5), years=np.arange(2020,2025)) # Scale up screening over 5 years starting in 2020

initialize(sim)

class campaign_screening(product=None, age_range=None, sex=None, eligibility=None, prob=None, years=None, interpolate=None, **kwargs)

Bases: BaseScreening, CampaignDelivery

Campaign screening - an instance of base screening combined with campaign delivery. See base classes for a description of input arguments.

Examples:

screen1 = hpv.campaign_screening(product='hpv', prob=0.2, years=2030) # Screen 20% of the eligible population in 2020
screen2 = hpv.campaign_screening(product='hpv', prob=0.02, years=[2025,2030]) # Screen 20% of the eligible population in 2025 and again in 2030

initialize(sim)

class BaseTriage(**kwargs)

Bases: BaseTest

Base class for triage.

Parameters:

kwargs (dict) – passed to BaseTest

check_eligibility(sim)

apply(sim)

class routine_triage(product=None, prob=None, eligibility=None, age_range=None, years=None, start_year=None, end_year=None, annual_prob=None, **kwargs)

Bases: BaseTriage, RoutineDelivery

Routine triage - an instance of base triage combined with routine delivery. See base classes for a description of input arguments.

Examples:

# Example 1: Triage 40% of the eligible population in all years
triage1 = hpv.routine_triage(product='via_triage', prob=0.4)

# Example 2: Triage positive screens into confirmatory testing or theapeutic vaccintion
screened_pos = lambda sim: sim.get_intervention('screening').outcomes['positive']
triage2 = hpv.routine_triage(product='pos_screen_assessment', eligibility=screen_pos, prob=0.9, start_year=2030)

initialize(sim)

class campaign_triage(product=None, age_range=None, sex=None, eligibility=None, prob=None, years=None, interpolate=None, annual_prob=None, **kwargs)

Bases: BaseTriage, CampaignDelivery

Campaign triage - an instance of base triage combined with campaign delivery. See base classes for a description of input arguments.

Examples:

# Example 1: In 2030, triage all positive screens into confirmatory testing or therapeutic vaccintion
screened_pos = lambda sim: sim.get_intervention('screening').outcomes['positive']
triage1 = hpv.campaign_triage(product='pos_screen_assessment', eligibility=screen_pos, prob=0.9, years=2030)

initialize(sim)

class BaseTreatment(product=None, prob=None, eligibility=None, age_range=None, **kwargs)

Bases: Intervention

Base treatment class.

Parameters:

• product (str/Product) – the treatment product to use

• accept_prob (float/arr) – acceptance rate of treatment - interpreted as the % of women eligble for treatment who accept

• eligibility (inds/callable) – indices OR callable that returns inds

• label (str) – the name of treatment strategy

• kwargs (dict) – passed to Intervention()

initialize(sim)

check_eligibility(sim)

Check people’s eligibility for treatment

get_accept_inds(sim)

Get indices of people who will acccept treatment; these people are then added to a queue or scheduled for receiving treatment

get_candidates(sim)

Get candidates for treatment on this timestep. Implemented by derived classes.

apply(sim)

Perform treatment by getting candidates, checking their eligibility, and then treating them.

class treat_num(max_capacity=None, **kwargs)

Bases: BaseTreatment

Treat a fixed number of people each timestep.

Parameters:

max_capacity (int) – maximum number who can be treated each timestep

add_to_queue(sim)

Add people who are willing to accept treatment to the queue

get_candidates(sim)

Get the indices of people who are candidates for treatment

apply(sim)

Apply treatment. On each timestep, this method will add eligible people who are willing to accept treatment to a queue, and then will treat as many people in the queue as there is capacity for.

class treat_delay(delay=None, **kwargs)

Bases: BaseTreatment

Treat people after a fixed delay

Parameters:

delay (int) – years of delay between becoming eligible for treatment and receiving treatment.

add_to_schedule(sim)

Add people who are willing to accept treatment to the treatment scehduler

get_candidates(sim)

Get the indices of people who are candidates for treatment

apply(sim)

Apply treatment. On each timestep, this method will add eligible people who are willing to accept treatment to a scheduler, and then will treat anyone scheduled for treatment on this timestep.

class BaseTxVx(**kwargs)

Bases: BaseTreatment

Base class for therapeutic vaccination

deliver(sim)

Deliver the intervention. This applies on a single timestep, whereas apply() methods apply on every timestep and can selectively call this method.

class routine_txvx(product=None, prob=None, age_range=None, eligibility=None, start_year=None, end_year=None, years=None, annual_prob=None, **kwargs)

Bases: BaseTxVx, RoutineDelivery

Routine delivery of therapeutic vaccine - an instance of treat_num combined

with routine delivery. See base classes for a description of input arguments.

Examples:

txvx1 = hpv.routine_txvx(product='txvx1', prob=0.9, age_range=[25,26], start_year=2030) # Vaccinate 90% of 25yo women every year starting 2025
txvx2 = hpv.routine_txvx(product='txvx1', prob=np.linspace(0.2,0.8,5), age_range=[25,26], years=np.arange(2030,2035)) # Scale up vaccination over 5 years starting in 2020

initialize(sim)

apply(sim)

class campaign_txvx(product=None, prob=None, age_range=None, eligibility=None, years=None, interpolate=True, annual_prob=None, **kwargs)

Bases: BaseTxVx, CampaignDelivery

Campaign delivery of therapeutic vaccine - an instance of treat_num combined with campaign delivery. See base classes for a description of input arguments.

initialize(sim)

apply(sim)

class linked_txvx(**kwargs)

Bases: BaseTxVx

Deliver therapeutic vaccine. This intervention should be used if TxVx delivery is linked to another program that determines eligibility, e.g. a screening program. Handling of dates is assumed to be handled by the linked intervention.

apply(sim)

class dx(df, hierarchy=None)

Bases: Product

Testing products are used within screening and triage. Their fundamental property is that they classify people into exactly one result state. They do not change anything about the People.

property default_value

administer(sim, inds, return_format='dict')

Administer a testing product.

Returns:

an array of length len(inds) with integer entries that map each person to one of the result_states if return_format==’dict’: a dictionary keyed by result_states with values containing the indices of people classified into this state

Return type:

if return_format==’array’

class tx(df, clearance=0.8, genotype_pars=None, imm_init=None, imm_boost=None)

Bases: Product

Treatment products include anything used to treat cancer or precancer, as well as therapeutic vaccination. They change fundamental properties about People, including their prognoses and infectiousness.

get_people_in_state(state, g, sim)

Find people within a given state/genotype. Returns indices

administer(sim, inds, return_format='dict')

Loop over treatment states to determine those who are successfully treated and clear infection

class vx(genotype_pars=None, imm_init=None, imm_boost=None)

Bases: Product

Vaccine product

administer(people, inds)

Apply the vaccine to the requested people indices.

class radiation(dur=None)

Bases: Product

administer(sim, inds)

default_dx(prod_name=None)

Create default diagnostic products

default_tx(prod_name=None)

Create default treatment products

default_vx(prod_name=None)

Create default vaccine products