# Socioeconomic Interventions for WHO’s End TB Strategy Targets: Insights from SIR Modelling in Kazakhstan

## TL;DR

This study uses a mathematical model to show how socioeconomic improvements can help reduce tuberculosis in Kazakhstan and meet global health targets.

## Contribution

The study introduces a novel SIR model linking socioeconomic factors to TB transmission, providing actionable insights for policymakers.

## Key findings

- A calibrated SIR model achieved 2.3% mean absolute percentage error in predicting TB incidence.
- Scenario simulations showed enhanced financial support for TB patients had the largest impact on reducing incidence.
- A combination of GDP growth, healthcare funding, and reduced poverty/unemployment could meet WHO’s 2030 TB targets.

## Abstract

Public health relevance—How does this work relate to a public health issue?
This study addresses the persistent public health challenge of tuberculosis in Kazakhstan by modeling its transmission dynamics, directly relating to the global issue of tuberculosis as one of leading causes of death.This work highlights how socioeconomic interventions can mitigate transmission risks in middle-income settings, thereby tackling inequities in health outcomes linked to poverty, unemployment, and limited healthcare access.

This study addresses the persistent public health challenge of tuberculosis in Kazakhstan by modeling its transmission dynamics, directly relating to the global issue of tuberculosis as one of leading causes of death.

This work highlights how socioeconomic interventions can mitigate transmission risks in middle-income settings, thereby tackling inequities in health outcomes linked to poverty, unemployment, and limited healthcare access.

Public health significance—Why is this work of significance to public health?
The model provides quantitative evidence on how targeted socioeconomic improvements can accelerate tuberculosis incidence reduction, offering a tool for policymakers to prioritize interventions and achieve WHO’s End TB Strategy targets.By linking transmission coefficients to socioeconomic predictors and demonstrating robust predictive accuracy, the work advances public health modelling, enabling better forecasting and scenario planning to prevent tuberculosis resurgence amid ongoing global health challenges.

The model provides quantitative evidence on how targeted socioeconomic improvements can accelerate tuberculosis incidence reduction, offering a tool for policymakers to prioritize interventions and achieve WHO’s End TB Strategy targets.

By linking transmission coefficients to socioeconomic predictors and demonstrating robust predictive accuracy, the work advances public health modelling, enabling better forecasting and scenario planning to prevent tuberculosis resurgence amid ongoing global health challenges.

Public health implications—What are the key implications or messages for practitioners, policy makers and/or researchers in public health?
For policymakers, the results emphasize prioritizing financial assistance for TB patients and providing actionable benchmarks to integrate socioeconomic measures into national TB control programs for meeting WHO targets.For researchers, the study highlights the value of incorporating socioeconomic factors into dynamic models, suggesting future extensions with drug-resistant strains and stochastic elements to enhance model applicability.

For policymakers, the results emphasize prioritizing financial assistance for TB patients and providing actionable benchmarks to integrate socioeconomic measures into national TB control programs for meeting WHO targets.

For researchers, the study highlights the value of incorporating socioeconomic factors into dynamic models, suggesting future extensions with drug-resistant strains and stochastic elements to enhance model applicability.

Background: Tuberculosis remains a major global public health challenge. Mathematical models are essential for strategic planning and evaluation of tuberculosis control programs, while addressing socioeconomic risk factors has proven key to accelerating incidence declines. Therefore, this study quantitatively assesses the impact of socioeconomic interventions on tuberculosis incidence in Kazakhstan. Methods: A modified SIR compartmental model was developed in Python 3.12 to simulate tuberculosis transmission dynamics. Parameters were calibrated using the Nelder–Mead simplex algorithm, and predictive performance was evaluated via hold-out validation. Scenario-based projections were generated to explore the impact of socioeconomic improvements on future tuberculosis incidence. Results: The calibrated SIR model demonstrated strong predictive accuracy, achieving a mean absolute percentage error of 2.3%. The sensitivity analysis revealed that the model is robust to moderate socioeconomic perturbations, with healthcare funding and unemployment rate as the primary uncertainty drivers. Scenario simulations showed that enhanced financial assistance for tuberculosis patients produced the largest effect beyond baseline. Optimization results indicate that 7.4% rise in GDP per capita, 10.2% increase in healthcare funding, 23.1% and 19.1% reductions in poverty and unemployment rates, and 40.2% growth in tuberculosis patient financial support relative to 2024 are sufficient to achieve the WHO’s End TB Strategy 2030 target. Conclusions: The model offers a valuable tool for tuberculosis forecasting and intervention evaluation, highlighting the synergistic role of socioeconomic measures in achieving global elimination goals.