Spatio-temporal spread of artemisinin resistance in Southeast Asia
Issued Date
2024-04-01
Resource Type
ISSN
1553734X
eISSN
15537358
Scopus ID
2-s2.0-85190856854
Pubmed ID
38626207
Journal Title
PLoS Computational Biology
Volume
20
Issue
4 April
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS Computational Biology Vol.20 No.4 April (2024)
Suggested Citation
Flegg J.A., Kandanaarachchi S., Guerin P.J., Dondorp A.M., Nosten F.H., Otienoburu S.D., Golding N. Spatio-temporal spread of artemisinin resistance in Southeast Asia. PLoS Computational Biology Vol.20 No.4 April (2024). doi:10.1371/journal.pcbi.1012017 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98124
Title
Spatio-temporal spread of artemisinin resistance in Southeast Asia
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
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. Copyright: