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Adaptive bandit algorithms increase efficiency of mobile tuberculosis screening programs.

Jiujia Zhang1, Lauren Linde2, Daniela Puma3

  • 1Boston University College of Engineering, Boston, USA.

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|December 8, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning using multi-armed bandit (MAB) algorithms optimized mobile tuberculosis screening locations. The LinUCB algorithm significantly improved detection efficiency, reducing screenings needed for tuberculosis cases.

Keywords:
Active case-findingAdaptive algorithmsMobile screening unitsMulti-arm banditsScreening yieldTuberculosis screening

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Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Machine Learning Applications

Background:

  • Community-based tuberculosis screening with mobile X-ray units enhances case detection by overcoming access barriers.
  • Optimizing the placement of mobile screening units is crucial for efficient resource allocation in tuberculosis control.

Purpose of the Study:

  • To evaluate the effectiveness of the multi-armed bandit (MAB) framework for optimizing mobile tuberculosis screening locations.
  • To compare the performance of two MAB algorithms (Exp3 and LinUCB) against traditional placement strategies.

Main Methods:

  • Simulations were conducted over three years for two mobile units serving 95 sites in Lima, Peru.
  • Two MAB algorithms, Exp3 and LinUCB, were compared with historical case-rate-driven and random placement strategies.
  • LinUCB incorporated local socioeconomic indicators, while both MAB algorithms adapted site selection based on observed screening yields.

Main Results:

  • MAB algorithms significantly reduced the average screenings needed per tuberculosis detection: 112 for Exp3 and 79 for LinUCB, versus 152 (random) and 143 (historical).
  • LinUCB demonstrated superior performance, achieving 20% increased detection efficiency by week 16 and 50% by week 40 compared to historical placement.
  • Both MAB algorithms showed improved tuberculosis screening yields, highlighting the benefits of adaptive, data-driven approaches.

Conclusions:

  • Multi-armed bandit algorithms effectively optimize mobile tuberculosis screening site selection, enhancing detection efficiency.
  • Adaptive machine learning models offer a promising strategy for improving resource allocation in high-burden tuberculosis settings.
  • The findings support the integration of data-driven approaches into public health screening programs for infectious diseases.