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Modelling to inform next-generation medical interventions for malaria prevention and treatment
Global progress against malaria has stagnated and novel medical interventions to prevent malaria are needed to fill gaps in existing tools and improve protection against infection and disease. Candidate selection for next-generation interventions should be supported by the best available evidence. T...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039673/ https://www.ncbi.nlm.nih.gov/pubmed/36966272 http://dx.doi.org/10.1038/s43856-023-00274-0 |
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author | Nekkab, Narimane Malinga, Josephine Braunack-Mayer, Lydia Kelly, Sherrie L. Miller, R. Scott Penny, Melissa A. |
author_facet | Nekkab, Narimane Malinga, Josephine Braunack-Mayer, Lydia Kelly, Sherrie L. Miller, R. Scott Penny, Melissa A. |
author_sort | Nekkab, Narimane |
collection | PubMed |
description | Global progress against malaria has stagnated and novel medical interventions to prevent malaria are needed to fill gaps in existing tools and improve protection against infection and disease. Candidate selection for next-generation interventions should be supported by the best available evidence. Target product profiles and preferred product characteristics play a key role in setting selection criteria requirements and early endorsement by health authorities. While clinical evidence and expert opinion often inform product development decisions, integrating modelling evidence early and iteratively into this process provides an opportunity to link product characteristics with expected public health outcomes. Population models of malaria transmission can provide a better understanding of which, and at what magnitude, key intervention characteristics drive public health impact, and provide quantitative evidence to support selection of use-cases, transmission settings, and deployment strategies. We describe how modelling evidence can guide and accelerate development of new malaria vaccines, monoclonal antibodies, and chemoprevention. |
format | Online Article Text |
id | pubmed-10039673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100396732023-03-27 Modelling to inform next-generation medical interventions for malaria prevention and treatment Nekkab, Narimane Malinga, Josephine Braunack-Mayer, Lydia Kelly, Sherrie L. Miller, R. Scott Penny, Melissa A. Commun Med (Lond) Perspective Global progress against malaria has stagnated and novel medical interventions to prevent malaria are needed to fill gaps in existing tools and improve protection against infection and disease. Candidate selection for next-generation interventions should be supported by the best available evidence. Target product profiles and preferred product characteristics play a key role in setting selection criteria requirements and early endorsement by health authorities. While clinical evidence and expert opinion often inform product development decisions, integrating modelling evidence early and iteratively into this process provides an opportunity to link product characteristics with expected public health outcomes. Population models of malaria transmission can provide a better understanding of which, and at what magnitude, key intervention characteristics drive public health impact, and provide quantitative evidence to support selection of use-cases, transmission settings, and deployment strategies. We describe how modelling evidence can guide and accelerate development of new malaria vaccines, monoclonal antibodies, and chemoprevention. Nature Publishing Group UK 2023-03-25 /pmc/articles/PMC10039673/ /pubmed/36966272 http://dx.doi.org/10.1038/s43856-023-00274-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Perspective Nekkab, Narimane Malinga, Josephine Braunack-Mayer, Lydia Kelly, Sherrie L. Miller, R. Scott Penny, Melissa A. Modelling to inform next-generation medical interventions for malaria prevention and treatment |
title | Modelling to inform next-generation medical interventions for malaria prevention and treatment |
title_full | Modelling to inform next-generation medical interventions for malaria prevention and treatment |
title_fullStr | Modelling to inform next-generation medical interventions for malaria prevention and treatment |
title_full_unstemmed | Modelling to inform next-generation medical interventions for malaria prevention and treatment |
title_short | Modelling to inform next-generation medical interventions for malaria prevention and treatment |
title_sort | modelling to inform next-generation medical interventions for malaria prevention and treatment |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039673/ https://www.ncbi.nlm.nih.gov/pubmed/36966272 http://dx.doi.org/10.1038/s43856-023-00274-0 |
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