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Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania
BACKGROUND: More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7053121/ https://www.ncbi.nlm.nih.gov/pubmed/32122342 http://dx.doi.org/10.1186/s12936-020-03173-0 |
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author | Runge, Manuela Molteni, Fabrizio Mandike, Renata Snow, Robert W. Lengeler, Christian Mohamed, Ally Pothin, Emilie |
author_facet | Runge, Manuela Molteni, Fabrizio Mandike, Renata Snow, Robert W. Lengeler, Christian Mohamed, Ally Pothin, Emilie |
author_sort | Runge, Manuela |
collection | PubMed |
description | BACKGROUND: More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps exist. Global and local policymakers, as well as funders, increasingly recognize the value of mathematical modelling as a strategic tool to support decision making. This case study article describes the long-term use of modelling in close collaboration with the National Malaria Control Programme (NMCP) in Tanzania, the challenges encountered and lessons learned. CASE DESCRIPTION: In Tanzania, a recent rebound in prevalence led to the revision of the national malaria strategic plan with interventions targeted to the malaria risk at the sub-regional level. As part of the revision, a mathematical malaria modelling framework for setting specific predictions was developed and used between 2016 and 2019 to (1) reproduce setting specific historical malaria trends, and (2) to simulate in silico the impact of future interventions. Throughout the project, multiple stakeholder workshops were attended and the use of mathematical modelling interactively discussed. EVALUATION: In Tanzania, the model application created an interdisciplinary and multisectoral dialogue platform between modellers, NMCP and partners and contributed to the revision of the national malaria strategic plan by simulating strategies suggested by the NMCP. The uptake of the modelling outputs and sustained interest by the NMCP were critically associated with following factors: (1) effective sensitization to the NMCP, (2) regular and intense communication, (3) invitation for the modellers to participate in the strategic plan process, and (4) model application tailored to the local context. CONCLUSION: Empirical data analysis and its use for strategic thinking remain the cornerstone for evidence-based decision-making. Mathematical impact modelling can support the process both by unifying all stakeholders in one strategic process and by adding new key evidence required for optimized decision-making. However, without a long-standing partnership, it will be much more challenging to sensibilize programmes to the usefulness and sustained use of modelling and local resources within the programme or collaborating research institutions need to be mobilized. |
format | Online Article Text |
id | pubmed-7053121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70531212020-03-10 Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania Runge, Manuela Molteni, Fabrizio Mandike, Renata Snow, Robert W. Lengeler, Christian Mohamed, Ally Pothin, Emilie Malar J Case Study BACKGROUND: More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps exist. Global and local policymakers, as well as funders, increasingly recognize the value of mathematical modelling as a strategic tool to support decision making. This case study article describes the long-term use of modelling in close collaboration with the National Malaria Control Programme (NMCP) in Tanzania, the challenges encountered and lessons learned. CASE DESCRIPTION: In Tanzania, a recent rebound in prevalence led to the revision of the national malaria strategic plan with interventions targeted to the malaria risk at the sub-regional level. As part of the revision, a mathematical malaria modelling framework for setting specific predictions was developed and used between 2016 and 2019 to (1) reproduce setting specific historical malaria trends, and (2) to simulate in silico the impact of future interventions. Throughout the project, multiple stakeholder workshops were attended and the use of mathematical modelling interactively discussed. EVALUATION: In Tanzania, the model application created an interdisciplinary and multisectoral dialogue platform between modellers, NMCP and partners and contributed to the revision of the national malaria strategic plan by simulating strategies suggested by the NMCP. The uptake of the modelling outputs and sustained interest by the NMCP were critically associated with following factors: (1) effective sensitization to the NMCP, (2) regular and intense communication, (3) invitation for the modellers to participate in the strategic plan process, and (4) model application tailored to the local context. CONCLUSION: Empirical data analysis and its use for strategic thinking remain the cornerstone for evidence-based decision-making. Mathematical impact modelling can support the process both by unifying all stakeholders in one strategic process and by adding new key evidence required for optimized decision-making. However, without a long-standing partnership, it will be much more challenging to sensibilize programmes to the usefulness and sustained use of modelling and local resources within the programme or collaborating research institutions need to be mobilized. BioMed Central 2020-03-02 /pmc/articles/PMC7053121/ /pubmed/32122342 http://dx.doi.org/10.1186/s12936-020-03173-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Case Study Runge, Manuela Molteni, Fabrizio Mandike, Renata Snow, Robert W. Lengeler, Christian Mohamed, Ally Pothin, Emilie Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania |
title | Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania |
title_full | Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania |
title_fullStr | Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania |
title_full_unstemmed | Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania |
title_short | Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania |
title_sort | applied mathematical modelling to inform national malaria policies, strategies and operations in tanzania |
topic | Case Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7053121/ https://www.ncbi.nlm.nih.gov/pubmed/32122342 http://dx.doi.org/10.1186/s12936-020-03173-0 |
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