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The role of mathematical modelling in guiding the science and economics of malaria elimination
Unprecedented efforts are now underway to eliminate malaria from many regions. Despite the enormous financial resources committed, if malaria elimination is perceived as failing it is likely that this funding will not be sustained. It is imperative that methods are developed to use the limited data...
Autores principales: | , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3004029/ https://www.ncbi.nlm.nih.gov/pubmed/21270957 http://dx.doi.org/10.1016/j.inhe.2010.09.005 |
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author | Maude, Richard J. Lubell, Yoel Socheat, Duong Yeung, Shunmay Saralamba, Sompob Pongtavornpinyo, Wirichada Cooper, Ben S. Dondorp, Arjen M. White, Nicholas J. White, Lisa J. |
author_facet | Maude, Richard J. Lubell, Yoel Socheat, Duong Yeung, Shunmay Saralamba, Sompob Pongtavornpinyo, Wirichada Cooper, Ben S. Dondorp, Arjen M. White, Nicholas J. White, Lisa J. |
author_sort | Maude, Richard J. |
collection | PubMed |
description | Unprecedented efforts are now underway to eliminate malaria from many regions. Despite the enormous financial resources committed, if malaria elimination is perceived as failing it is likely that this funding will not be sustained. It is imperative that methods are developed to use the limited data available to design site-specific, cost-effective elimination programmes. Mathematical modelling is a way of including mechanistic understanding to use available data to make predictions. Different strategies can be evaluated much more rapidly than is possible through trial and error in the field. Mathematical modelling has great potential as a tool to guide and inform current elimination efforts. Economic modelling weighs costs against characterised effects or predicted benefits in order to determine the most cost-efficient strategy but has traditionally used static models of disease not suitable for elimination. Dynamic mathematical modelling and economic modelling techniques need to be combined to contribute most effectively to ongoing policy discussions. We review the role of modelling in previous malaria control efforts as well as the unique nature of elimination and the consequent need for its explicit modelling, and emphasise the importance of good disease surveillance. The difficulties and complexities of economic evaluation of malaria control, particularly the end stages of elimination, are discussed. |
format | Text |
id | pubmed-3004029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-30040292011-01-24 The role of mathematical modelling in guiding the science and economics of malaria elimination Maude, Richard J. Lubell, Yoel Socheat, Duong Yeung, Shunmay Saralamba, Sompob Pongtavornpinyo, Wirichada Cooper, Ben S. Dondorp, Arjen M. White, Nicholas J. White, Lisa J. Int Health Review Unprecedented efforts are now underway to eliminate malaria from many regions. Despite the enormous financial resources committed, if malaria elimination is perceived as failing it is likely that this funding will not be sustained. It is imperative that methods are developed to use the limited data available to design site-specific, cost-effective elimination programmes. Mathematical modelling is a way of including mechanistic understanding to use available data to make predictions. Different strategies can be evaluated much more rapidly than is possible through trial and error in the field. Mathematical modelling has great potential as a tool to guide and inform current elimination efforts. Economic modelling weighs costs against characterised effects or predicted benefits in order to determine the most cost-efficient strategy but has traditionally used static models of disease not suitable for elimination. Dynamic mathematical modelling and economic modelling techniques need to be combined to contribute most effectively to ongoing policy discussions. We review the role of modelling in previous malaria control efforts as well as the unique nature of elimination and the consequent need for its explicit modelling, and emphasise the importance of good disease surveillance. The difficulties and complexities of economic evaluation of malaria control, particularly the end stages of elimination, are discussed. Oxford University Press 2010-12 /pmc/articles/PMC3004029/ /pubmed/21270957 http://dx.doi.org/10.1016/j.inhe.2010.09.005 Text en © 2010 Published by Elsevier Ltd on behalf of Royal Society of Tropical Medicine and Hygiene. https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Review Maude, Richard J. Lubell, Yoel Socheat, Duong Yeung, Shunmay Saralamba, Sompob Pongtavornpinyo, Wirichada Cooper, Ben S. Dondorp, Arjen M. White, Nicholas J. White, Lisa J. The role of mathematical modelling in guiding the science and economics of malaria elimination |
title | The role of mathematical modelling in guiding the science and economics of malaria elimination |
title_full | The role of mathematical modelling in guiding the science and economics of malaria elimination |
title_fullStr | The role of mathematical modelling in guiding the science and economics of malaria elimination |
title_full_unstemmed | The role of mathematical modelling in guiding the science and economics of malaria elimination |
title_short | The role of mathematical modelling in guiding the science and economics of malaria elimination |
title_sort | role of mathematical modelling in guiding the science and economics of malaria elimination |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3004029/ https://www.ncbi.nlm.nih.gov/pubmed/21270957 http://dx.doi.org/10.1016/j.inhe.2010.09.005 |
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