Enhanced TB Treatment Interventions¶

This module provides comprehensive TB treatment interventions including DOTS implementation, enhanced treatment protocols, and drug regimen management.

Enhanced TB Treatment Class¶

Treatment Factory Functions¶

Model Overview¶

The Enhanced TB Treatment module implements comprehensive treatment interventions for tuberculosis with the following key features:

Treatment Protocols

DOTS Implementation: Directly Observed Treatment, Short-course
Enhanced DOTS: Improved treatment with better support and monitoring
First Line Treatment: Standard combination therapy for drug-sensitive TB
Custom Regimens: Configurable treatment parameters and protocols

Treatment Management

Individual treatment assignment and tracking
Treatment duration and adherence modeling
Treatment outcome monitoring
Drug resistance considerations

Intervention Features

Age and risk factor targeting
Dynamic treatment initiation
Treatment effectiveness tracking
Integration with diagnostic systems

Key Features¶

DOTS Implementation

Standard WHO-recommended DOTS protocol
Treatment observation and support
Standardized drug regimens
Outcome monitoring and reporting

Enhanced Treatment Options

Improved DOTS with additional support
First-line combination therapy
Customizable treatment parameters
Risk-stratified treatment approaches

Treatment Tracking

Individual treatment status
Treatment duration monitoring
Adherence tracking
Outcome assessment

Integration Capabilities

Diagnostic system integration
Comorbidity consideration
Network-based targeting
Dynamic parameter adjustment

Usage Examples¶

Basic DOTS implementation:

from tbsim.interventions.enhanced_tb_treatment import create_dots_treatment

# Create standard DOTS treatment
dots = create_dots_treatment()
sim.add_intervention(dots)
sim.run()

Enhanced DOTS treatment:

from tbsim.interventions.enhanced_tb_treatment import create_dots_improved_treatment

# Create enhanced DOTS with better support
enhanced_dots = create_dots_improved_treatment()
sim.add_intervention(enhanced_dots)
sim.run()

First-line combination therapy:

from tbsim.interventions.enhanced_tb_treatment import create_first_line_treatment

# Create first-line combination treatment
first_line = create_first_line_treatment()
sim.add_intervention(first_line)
sim.run()

Custom treatment parameters:

from tbsim.interventions.enhanced_tb_treatment import EnhancedTBTreatment

# Create custom treatment intervention
custom_treatment = EnhancedTBTreatment(pars={
    'treatment_duration': 180,      # 6 months
    'success_rate': 0.85,           # 85% success rate
    'target_age_min': 15,           # Minimum age 15
    'target_age_max': 65,           # Maximum age 65
    'start_date': '2020-01-01',    # Start date
    'stop_date': '2030-12-31'      # Stop date
})

sim.add_intervention(custom_treatment)
sim.run()

Treatment Monitoring¶

Individual Tracking

Treatment initiation dates
Current treatment status
Treatment duration
Adherence patterns

Population Level Metrics

Treatment coverage rates
Treatment success rates
Treatment completion rates
Drug resistance patterns

Outcome Assessment

Cure rates
Treatment failure rates
Relapse rates
Mortality rates

Integration with Other Modules¶

Diagnostic Integration

Treatment initiation based on diagnostic results
Treatment type selection based on diagnostic findings
Integration with enhanced diagnostic systems

Comorbidity Considerations

HIV status consideration in treatment selection
Nutritional status effects on treatment outcomes
Age-specific treatment protocols

Network Effects

Household-based treatment targeting
Community treatment programs
Contact tracing integration

For detailed information about specific methods and parameters, see the individual class documentation above. All methods include comprehensive mathematical models and implementation details in their docstrings.