Predicting communities with hightuberculosis case-finding efficiency tooptimise resource allocation in Pakistan: comparing the performance of anegative binomial spatial lag modelwith a Bayesian machine-learningmodel
- Authors
- Christina Mergenthaler, Jake D. Mathewson, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Abdullah Latif, Hasan Tahir, Tanveer Ahmed, Zia Samad, Frank Cobelens, Daniella Brals, Mirjam I Bakker, Ente Rood
- Publication year
- February 2025
Despite progress in tuberculosis (TB) treatment coverage in past years, an estimated 183000 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 probability of having undetected TB. The aim of this study was to cross-validate TB positivity rate predictions in ACF settings of an existing Bayesian machine learning (BML) with a simpler frequentist model.
