Basic TB Treatment

This module provides basic TB treatment functionality for TBsim simulations.

Main TB Treatment Module

class tbsim.interventions.tb_treatment.EnhancedTBTreatment(pars=None, **kwargs)[source]

Bases: Intervention

Enhanced TB treatment intervention that implements configurable drug types and treatment protocols.

This intervention manages the treatment of diagnosed TB cases by: - Selecting eligible individuals for treatment - Applying drug-specific treatment protocols - Tracking treatment outcomes (success/failure) - Managing post-treatment care-seeking behavior - Recording comprehensive treatment statistics

Parameters:

  • drug_type (TBDrugType) – The type of drug regimen to use for treatment. Options include: - DOTS: Standard DOTS protocol - DOTS_IMPROVED: Enhanced DOTS with better adherence - FIRST_LINE_COMBO: First-line combination therapy

  • treatment_success_rate (float, default 0.85) – Base treatment success rate (can be overridden by drug-specific parameters)

  • reseek_multiplier (float, default 2.0) – Multiplier applied to care-seeking probability after treatment failure

  • reset_flags (bool, default True) – Whether to reset diagnosis and testing flags after treatment failure

drug_parameters

Drug-specific parameters including cure rates and side effects

Type:

TBDrugTypeParameters

new_treated

List of UIDs of individuals who started treatment in the current timestep

Type:

list

successes

List of UIDs of individuals who successfully completed treatment

Type:

list

failures

List of UIDs of individuals who failed treatment

Type:

list

drug_type_assignments

Mapping of individual UIDs to assigned drug types

Type:

dict

Examples

Create a standard DOTS treatment intervention:

>>> treatment = EnhancedTBTreatment(drug_type=TBDrugType.DOTS)

Create a first-line combination treatment with custom parameters:

>>> treatment = EnhancedTBTreatment(
...     drug_type=TBDrugType.FIRST_LINE_COMBO,
...     reseek_multiplier=3.0,
...     reset_flags=False
... )

Track treatment outcomes over time:

>>> treatment = EnhancedTBTreatment()
>>> # After running simulation
>>> cumulative_success = treatment.results['cum_treatment_success']
>>> treatment_failures = treatment.results['n_treatment_failure']
__init__(pars=None, **kwargs)[source]

Initialize the enhanced TB treatment intervention.

Parameters:

  • pars (dict, optional) – Dictionary of parameters to override defaults

  • **kwargs – Additional keyword arguments passed to parent class

step()[source]

Execute one timestep of enhanced TB treatment.

This method performs the following operations: 1. Identifies eligible individuals (diagnosed with active TB) 2. Initiates treatment for eligible cases 3. Determines treatment outcomes based on drug-specific success rates 4. Updates individual states and flags based on outcomes 5. Manages post-treatment care-seeking behavior 6. Records treatment statistics for analysis

The treatment process follows standard TB treatment protocols where: - Successful treatment clears TB infection and restores susceptibility - Failed treatment triggers renewed care-seeking with increased probability - Treatment history is tracked for epidemiological analysis

init_results()[source]

Initialize results tracking for the intervention.

This method sets up the data structures needed to track: - Number of individuals treated per timestep - Treatment success and failure counts - Cumulative treatment outcomes over time - Drug type used in each timestep

The results are designed to facilitate epidemiological analysis and intervention effectiveness evaluation.

update_results()[source]

Update results for the current timestep.

This method records the treatment outcomes from the current timestep and maintains running totals for cumulative analysis. It processes: - Current timestep treatment counts - Success and failure outcomes - Drug type information - Cumulative totals for epidemiological analysis

The results are stored in the intervention’s results dictionary and can be accessed for post-simulation analysis and visualization.

Available Classes

TBTreatment

Basic TB treatment intervention with success/failure logic

Key Features

  • Treatment Initiation: Start treatment for diagnosed TB cases

  • Success/Failure Logic: Realistic treatment outcome modeling

  • Care-seeking Behavior: Post-treatment health-seeking patterns

  • Flag Management: Reset diagnosis and testing flags after failure

  • Result Tracking: Comprehensive treatment outcome monitoring

Usage Examples

Basic TB treatment:

from tbsim.interventions.tb_treatment import TBTreatment
from tbsim import TB

# Add TB module and basic treatment
# Note: TBTreatment requires 'diagnosed' attribute
# This is typically added by diagnostic interventions
tb = TB()
treatment = TBTreatment()

sim = ss.Sim(
    diseases=tb,
    interventions=treatment
)
sim.run()

Custom treatment parameters:

treatment = TBTreatment(
    treatment_success_rate=0.9,  # 90% success rate
    reseek_multiplier=3.0,       # 3x care-seeking after failure
    reset_flags=True             # Reset diagnosis flags
)

Key Methods

Treatment Management

  • step(): Execute treatment logic each time step

  • init_results(): Initialize treatment result tracking

  • update_results(): Update results during simulation

Treatment Logic

  • Automatically identifies diagnosed TB cases

  • Applies treatment success/failure probabilities

  • Manages post-treatment care-seeking behavior

  • Tracks treatment outcomes and statistics

Treatment Outcomes

The module handles: - Treatment Success: Complete TB clearance and recovery - Treatment Failure: Failed treatment with renewed care-seeking - Care-seeking Multipliers: Increased health-seeking after failure - Flag Management: Reset of diagnosis and testing status

For advanced treatment capabilities, see the Enhanced TB Treatment Interventions module.