Spatio-temporal spread of artemisinin resistance in Southeast Asia.

IF 4.3 2区生物学

PLoS Computational Biology Pub Date : 2024-04-16 DOI:10.1371/journal.pcbi.1012017

Jennifer A Flegg, Sevvandi Kandanaarachchi, P. Guerin, A. Dondorp, François H. Nosten, S. D. Otienoburu, Nick Golding

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引用次数: 0

Abstract

Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse. We use a comprehensive database from the WorldWide Antimalarial Resistance Network on the prevalence of non-synonymous mutations in the Kelch 13 (K13) gene, which are known to be associated with artemisinin resistance, and a Bayesian geostatistical model to produce spatio-temporal predictions of artemisinin resistance. Our maps of estimated prevalence show an expansion of the K13 mutation across the Greater Mekong Subregion from 2000 to 2022. Moreover, the period between 2010 and 2015 demonstrated the most spatial change across the region. Our model and maps provide important insights into the spatial and temporal trends of artemisinin resistance in a way that is not possible using data alone, thereby enabling improved spatial decision support systems on an unprecedented fine-scale spatial resolution. By predicting for the first time spatio-temporal patterns and extents of artemisinin resistance at the subcontinent level, this study provides critical information for supporting malaria elimination goals in Southeast Asia.

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青蒿素抗药性在东南亚的时空传播。

目前的消除疟疾目标必须经受住巨大的挑战--目前的金标准抗疟药物（即青蒿素衍生物）的抗药性。如果青蒿素抗药性大幅扩展到非洲或印度，病例和与疟疾相关的死亡人数必将大幅增加。因此，有关东南亚地区青蒿素抗药性水平变化的空间信息对于卫生机构优先采取疟疾控制措施至关重要，但现有的青蒿素抗药性数据却很稀少。我们利用世界抗疟网络（WorldWide Antimalarial Resistance Network）关于Kelch 13（K13）基因非同义突变流行率的综合数据库，以及贝叶斯地理统计模型，对青蒿素抗药性进行了时空预测。我们的估计流行率地图显示，从2000年到2022年，K13突变在整个大湄公河次区域都在扩大。此外，2010 年至 2015 年期间整个地区的空间变化最大。我们的模型和地图为了解青蒿素抗药性的时空趋势提供了重要依据，而这一点仅靠数据是无法实现的，因此可以在前所未有的精细空间分辨率上改进空间决策支持系统。这项研究首次在次大陆一级预测了青蒿素抗药性的时空模式和范围，为支持东南亚实现消除疟疾的目标提供了重要信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology 生物-生化研究方法

CiteScore

7.10

自引率

4.70%

发文量

820

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