Cargando…
Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions
BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and t...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167503/ https://www.ncbi.nlm.nih.gov/pubmed/35659301 http://dx.doi.org/10.1186/s40249-022-00981-1 |
_version_ | 1784720812933120000 |
---|---|
author | Golumbeanu, Monica Yang, Guo-Jing Camponovo, Flavia Stuckey, Erin M. Hamon, Nicholas Mondy, Mathias Rees, Sarah Chitnis, Nakul Cameron, Ewan Penny, Melissa A. |
author_facet | Golumbeanu, Monica Yang, Guo-Jing Camponovo, Flavia Stuckey, Erin M. Hamon, Nicholas Mondy, Mathias Rees, Sarah Chitnis, Nakul Cameron, Ewan Penny, Melissa A. |
author_sort | Golumbeanu, Monica |
collection | PubMed |
description | BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. METHODS: A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. RESULTS: We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. CONCLUSIONS: Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-022-00981-1. |
format | Online Article Text |
id | pubmed-9167503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91675032022-06-06 Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions Golumbeanu, Monica Yang, Guo-Jing Camponovo, Flavia Stuckey, Erin M. Hamon, Nicholas Mondy, Mathias Rees, Sarah Chitnis, Nakul Cameron, Ewan Penny, Melissa A. Infect Dis Poverty Research Article BACKGROUND: Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. METHODS: A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. RESULTS: We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. CONCLUSIONS: Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40249-022-00981-1. BioMed Central 2022-06-04 /pmc/articles/PMC9167503/ /pubmed/35659301 http://dx.doi.org/10.1186/s40249-022-00981-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Research Article Golumbeanu, Monica Yang, Guo-Jing Camponovo, Flavia Stuckey, Erin M. Hamon, Nicholas Mondy, Mathias Rees, Sarah Chitnis, Nakul Cameron, Ewan Penny, Melissa A. Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
title | Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
title_full | Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
title_fullStr | Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
title_full_unstemmed | Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
title_short | Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
title_sort | leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167503/ https://www.ncbi.nlm.nih.gov/pubmed/35659301 http://dx.doi.org/10.1186/s40249-022-00981-1 |
work_keys_str_mv | AT golumbeanumonica leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT yangguojing leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT camponovoflavia leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT stuckeyerinm leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT hamonnicholas leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT mondymathias leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT reessarah leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT chitnisnakul leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT cameronewan leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions AT pennymelissaa leveragingmathematicalmodelsofdiseasedynamicsandmachinelearningtoimprovedevelopmentofnovelmalariainterventions |