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TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan

  • Sandra Alba
  • , Ente Rood
  • , Fulvia Mecatti
  • , Jennifer M. Ross
  • , Peter J. Dodd
  • , Stewart Chang
  • , Matthys Potgieter
  • , Gaia Bertarelli
  • , Nathaniel J. Henry
  • , Kate E. Legrand
  • , William Trouleau
  • , Debebe Shaweno
  • , Peter MacPherson
  • , Zhi Zhen Qin
  • , Christina Mergenthaler
  • , Federica Giardina
  • , Ellen Wien Augustijn
  • , Aurangzaib Quadir Baloch
  • , Abdullah Latif
  • Royal Tropical Institute
  • University of Milan - Bicocca
  • University of Washington
  • University of Sheffield
  • Institute for Disease Modeling
  • Epcon
  • Sant'Anna School of Advanced Studies
  • University of Oxford
  • Swiss Federal Institute of Technology Lausanne
  • Queen Elizabeth Central Hospital Malawi
  • London School of Hygiene and Tropical Medicine
  • Stop TB Partnership
  • Radboud University Nijmegen
  • University of Twente
  • Pakistan National Tuberculosis Control Programme

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Pakistan’s national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates

of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010–2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010–2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the

geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the

challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to

subnational disease modelling. The NTP’s use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

Original languageEnglish
Article number13
Pages (from-to)13
JournalTropical Medicine and Infectious Disease
Volume7
Issue number1
DOIs
Publication statusPublished - 17 Jan 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Forecasting
  • Predictive modelling
  • Small area estimation
  • Spatial epidemiology
  • Subnational prevalence
  • Tuberculosis burden

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