Interventions

STIsim separates the product (what is being delivered) from the intervention (how it is delivered). A diagnostic product stores the characteristics of a test -- its sensitivity, specificity, and what states it can detect. An intervention stores the delivery information -- who is eligible, how often they are reached, and when the program starts and stops. This separation means you can swap out a diagnostic product without changing the delivery strategy, or target the same product to different populations.

Architecture

Intervention (delivery)          Intervention (delivery)
  - eligibility                    - eligibility
  - test probability               - treatment efficacy
  - start/stop years               - start/stop years
        │                                │
        ▼                                ▼
  Diagnostic product               Disease module
  (product characteristics)        (clears infection)
  - sensitivity by state
  - specificity

Testing and treatment are separate interventions. A testing intervention uses a diagnostic product to determine outcomes, and can optionally trigger a treatment intervention for positive results. Alternatively, treatment can be applied directly (e.g., syndromic management without a lab test).

Testing

STITest

The base testing class. Controls who gets tested, how often, and with what diagnostic.

test = sti.STITest(
    product=my_diagnostic,      # Diagnostic product (required)
    test_prob_data=0.1,         # Per-timestep probability of testing
    eligibility=lambda sim: sim.people.female,  # Who is eligible
    start=2015,                 # When testing begins
)

Key parameters:

Parameter	Default	Description
product	(required)	Diagnostic product (e.g., STIDx, HIVDx)
test_prob_data	1.0	Testing probability -- scalar, array, or time-varying
eligibility	all agents	Function f(sim) -> UIDs defining who can be tested
start / stop	sim start/end	Active period for the intervention
dt_scale	True	Scale probability by timestep length

HIVTest

HIV-specific testing. Defaults to testing only undiagnosed agents and using a perfect diagnostic.

hiv_test = sti.HIVTest(test_prob_data=0.1, start=2000)

On a positive result, sets sim.diseases.hiv.diagnosed = True.

SymptomaticTesting

Syndromic management: test symptomatic care-seekers and route them to treatment based on results. Used for bacterial STIs where lab confirmation may not be available.

syndromic = sti.SymptomaticTesting(
    diseases=[ng, ct, tv],
    treatments=[ng_tx, ct_tx, metro],
    disease_treatment_map={'ng': ng_tx, 'ct': ct_tx, 'tv': metro},
)

Treatment

STITreatment

The base treatment class. Clears infection for eligible agents with a given efficacy.

ct_tx = sti.STITreatment(
    diseases='ct',         # Which disease(s) to treat
    treat_eff=0.95,        # 95% efficacy
    name='ct_tx',
)

Key parameters:

Parameter	Default	Description
diseases	(required)	Disease name or list of names to treat
treat_prob	1.0	Probability of accepting treatment
treat_eff	0.9	Treatment efficacy
eligibility	all agents	Function defining who can be treated

Treatment outcomes are tracked as successful (infection cleared), failed (infection persists), or unnecessary (treated a susceptible agent).

Disease-specific treatments

Class	Disease	Notes
sti.STITreatment	Any STI	Generic; specify disease(s) by name
sti.GonorrheaTreatment	Gonorrhea	Tracks antimicrobial resistance via rel_treat
sti.SyphTx	Syphilis	Treats pregnant mothers and fetuses; stage-specific
sti.treat_BV	BV	CST-based treatment with durable post-treatment effects

HIV-specific interventions

ART

Antiretroviral therapy with CD4-based prioritization and coverage targets.

art = sti.ART(coverage_data=art_df)  # DataFrame with year index and n_art or p_art column

ART reduces transmissibility by 96% (default) and reconstitutes CD4 counts. Coverage can be specified as absolute numbers or proportions.

VMMC

Voluntary medical male circumcision. Reduces male susceptibility to HIV by 60% (default).

vmmc = sti.VMMC(coverage_data=vmmc_df)

PrEP

Pre-exposure prophylaxis targeting FSWs. Reduces susceptibility by 80% (default).

prep = sti.Prep(coverage=[0, 0.5], years=[2020, 2025])

Combining interventions

Interventions are passed to the sim as a list:

sim = sti.Sim(
    diseases='ng',
    interventions=[test, treatment],
)

For a complete worked example, see the Interventions tutorial.