A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping

Cheng, Sibo; Argaud, Jean-Philippe; Iooss, Bertrand; Ponçot, Angélique; Lucor, Didier

doi:10.1007/s11004-021-09951-z

Cited by 14 publications

(4 citation statements)

References 52 publications

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“…Note that without emulators, we would have to run these simulations in OpenMalaria, which would not have been feasible due to computational requirements. We then used the function soboljansen from the R package ‘sensitivity’ to perform the global sensitivity analysis with 150,000 bootstrap replicates and the two datasets ( Cheng et al, 2021 ). With this function, we estimated first-order and total Sobol' indices simultaneously.…”

Section: Methodsmentioning

confidence: 99%

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

Masserey

Lee

Golumbeanu

et al. 2022

eLife

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The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure—treatment properties, biological factors, transmission intensity, and access to treatment—obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.

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Section: Methodsmentioning

confidence: 99%

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

Masserey

Lee

Golumbeanu

et al. 2022

eLife

View full text Add to dashboard Cite

show abstract

“…Then, we predicted the selection coefficient for each parameter combination using the emulators that could efficiently predict the selection coefficient for all these parameter combinations compared to our model. Finally, we performed the global sensitivity analysis on the datasets with 150,000 bootstrap replicates using the function soboljansen from the R-package Sensitivity 29 . This function returned the first-order indices of each factor, representing their relative influence on the selection coefficient (Fig.…”

Section: Supplementary Informationmentioning

confidence: 99%

The selective pressure induced by malaria vaccines onPlasmodium falciparum

Masserey,

Lee,

Cavelan

et al. 2024

Preprint

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Antigens of Plasmodium falciparum targeted by most current and future vaccines are generally not conserved. There are limited studies estimating the risk of parasite selection with vaccines. We adapted an individual-based model of malaria to assess which conditions favour genotypes having some degree of vaccine resistance and estimate the impact of resistance spread on vaccine effectiveness. Even parasite genotypes with low degrees of vaccine resistance are likely to spread relatively quickly if vaccines are implemented in children and even faster in a broader population. However, only highly resistant genotypes could strongly reduce vaccine effectiveness. These results highlight that it is essential to understand the degree to which genotypes exhibit reduced sensitivity to vaccines and monitor genotype frequency and vaccine effectiveness along with vaccine deployment. If some genotypes exhibit reduced efficacy to vaccines, our results further suggest that polyvalent or combination vaccines should be considered to limit resistance spread.

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“…In past two years, online LA has raised significant research attention. For sparse and unstructured data, domain decomposition techniques [228] can also be used to reduce the problem dimension, for example, via community detection through a connection graph [229], [230].…”

Section: B ML and Da With Rommentioning

confidence: 99%

Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

Cheng¹,

Quilodran-Casas²,

Ouala³

et al. 2023

Preprint

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Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.

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A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping

Cited by 14 publications

References 52 publications

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

The selective pressure induced by malaria vaccines onPlasmodium falciparum

Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

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