TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan
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 developmen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Tropical Medicine and Infectious Disease |
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| Online Access: | https://www.mdpi.com/2414-6366/7/1/13 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Sandra Alba |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:24:26Z |
| format | Article |
| id | doaj.art-1849a85c47314a5d8d8d82bd1d253e85 |
| institution | Directory Open Access Journal |
| issn | 2414-6366 |
| language | English |
| last_indexed | 2024-03-10T00:24:26Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tropical Medicine and Infectious Disease |
| spelling | doaj.art-1849a85c47314a5d8d8d82bd1d253e852023-11-23T15:36:43ZengMDPI AGTropical Medicine and Infectious Disease2414-63662022-01-01711310.3390/tropicalmed7010013TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in PakistanSandra Alba0Ente Rood1Fulvia Mecatti2Jennifer M. Ross3Peter J. Dodd4Stewart Chang5Matthys Potgieter6Gaia Bertarelli7Nathaniel J. Henry8Kate E. LeGrand9William Trouleau10Debebe Shaweno11Peter MacPherson12Zhi Zhen Qin13Christina Mergenthaler14Federica Giardina15Ellen-Wien Augustijn16Aurangzaib Quadir Baloch17Abdullah Latif18KIT Royal Tropical Institute, 1092 AD Amsterdam, The NetherlandsKIT Royal Tropical Institute, 1092 AD Amsterdam, The NetherlandsDepartment of Sociology and Social Sciences, University of Milano Bicocca, 20126 Milan, ItalyDepartments of Global Health and Medicine, University of Washington, Seattle, WA 98195, USASchool of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UKInstitute for Disease Modeling, Seattle, WA 98109, USAEpcon, 2000 Antwerp, BelgiumSant’Anna School of Advanced Studies, 56127 Pisa, ItalyBig Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UKInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98109, USAÉcole Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandSchool of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UKMalawi-Liverpool-Wellcome Trust Clinical Research Programme, Queen Elizabeth Central Hospital, Blantyre 312225, MalawiStop TB Partnership, 1218 Geneva, SwitzerlandKIT Royal Tropical Institute, 1092 AD Amsterdam, The NetherlandsDepartment of Biostatistics, Radboud University Medical Centre, 6525 GA Nijmegen, The NetherlandsFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The NetherlandsPakistan National Tuberculosis Control Programme, Islamabad 44000, PakistanPakistan National Tuberculosis Control Programme, Islamabad 44000, PakistanPakistan’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.https://www.mdpi.com/2414-6366/7/1/13small area estimationtuberculosis burdenpredictive modellingsubnational prevalencespatial epidemiologyforecasting |
| spellingShingle | 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 TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan Tropical Medicine and Infectious Disease small area estimation tuberculosis burden predictive modelling subnational prevalence spatial epidemiology forecasting |
| title | TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan |
| title_full | TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan |
| title_fullStr | TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan |
| title_full_unstemmed | TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan |
| title_short | TB Hackathon: Development and Comparison of Five Models to Predict Subnational Tuberculosis Prevalence in Pakistan |
| title_sort | tb hackathon development and comparison of five models to predict subnational tuberculosis prevalence in pakistan |
| topic | small area estimation tuberculosis burden predictive modelling subnational prevalence spatial epidemiology forecasting |
| url | https://www.mdpi.com/2414-6366/7/1/13 |
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