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

Statistical models can be effective in predicting TB hotspots for ACF planning, and the relatively simpler NBR model was nearly as effective as a more complex BML model. The predictions of different modelling approaches were similar, suggesting that predictions are more driven by covariates rather than modelling framework. The agreement between model results increases confidence in the potential utility of models to spatially target ACF activities in high need, low access areas.

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Publication Details

Journal
BMJ Public Health
Published
2025-01-01
DOI
https://doi.org/10.1136/bmjph-2024-001424
Primary Topic
Pneumonia and Respiratory Infections
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Vincent Meurrens, Christina Mergenthaler, Frank G. J. Cobelens, Ente Rood et al.
BMJ Public Health
Pneumonia and Respiratory Infections
article

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

Vincent Meurrens, Christina Mergenthaler, Frank G. J. Cobelens, Ente Rood, Daniëlla Brals, Zainab Samad, Mirjam I. Bakker, Hasan Tahir, Andreas Werle van der Merwe, Jake D. Mathewson, Matthys Potgieter, Abdullah Latif, Tanveer Ahmed, Stephanie Lako
article en

Abstract

Statistical models can be effective in predicting TB hotspots for ACF planning, and the relatively simpler NBR model was nearly as effective as a more complex BML model. The predictions of different modelling approaches were similar, suggesting that predictions are more driven by covariates rather than modelling framework. The agreement between model results increases confidence in the potential utility of models to spatially target ACF activities in high need, low access areas.

BMJ Public HealthVol. 3(1)
Global Fund to Fight AIDS, Tuberculosis and Malaria (CH), Royal Tropical Institute (NL), South African Heart Association (ZA), University of Amsterdam (NL)
Openalex Percentile: Top 92%
Pneumonia and Respiratory Infections
DOIOpen Access PDFOpenAlex
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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 — Vincent Meurrens, Christina Mergenthaler, et al. · BMJ Public Health (2025) | TGRS Research Map | TGRS