Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag model with a Bayesian machine-learning model

Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag model with a Bayesian machine-learning model

Introduction Despite progress in tuberculosis (TB) treatment coverage in past years, an estimated 183 000 people with TB may not have been diagnosed in Pakistan in 2022. Therefore, there is a need to develop models which help to steer active case finding (ACF) towards populations with a high probabi...

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Bibliographic Details
Main Authors: Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood
Format: Article
Language:English
Published: BMJ Publishing Group 2025-05-01
Series:BMJ Public Health
Online Access:https://bmjpublichealth.bmj.com/content/3/1/e001424.full
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