Spatio-temporal spread of artemisinin resistance in Southeast Asia

dc.contributor.authorFlegg J.A.
dc.contributor.authorKandanaarachchi S.
dc.contributor.authorGuerin P.J.
dc.contributor.authorDondorp A.M.
dc.contributor.authorNosten F.H.
dc.contributor.authorOtienoburu S.D.
dc.contributor.authorGolding N.
dc.contributor.correspondenceFlegg J.A.
dc.contributor.otherMahidol University
dc.date.accessioned2024-04-27T18:09:17Z
dc.date.available2024-04-27T18:09:17Z
dc.date.issued2024-04-01
dc.description.abstractCurrent 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:
dc.identifier.citationPLoS Computational Biology Vol.20 No.4 April (2024)
dc.identifier.doi10.1371/journal.pcbi.1012017
dc.identifier.eissn15537358
dc.identifier.issn1553734X
dc.identifier.pmid38626207
dc.identifier.scopus2-s2.0-85190856854
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/98124
dc.rights.holderSCOPUS
dc.subjectMathematics
dc.subjectEnvironmental Science
dc.subjectNeuroscience
dc.subjectBiochemistry, Genetics and Molecular Biology
dc.subjectAgricultural and Biological Sciences
dc.subjectComputer Science
dc.titleSpatio-temporal spread of artemisinin resistance in Southeast Asia
dc.typeArticle
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85190856854&origin=inward
oaire.citation.issue4 April
oaire.citation.titlePLoS Computational Biology
oaire.citation.volume20
oairecerif.author.affiliationInfectious Diseases Data Observatory
oairecerif.author.affiliationWorldWide Antimalarial Resistance Network
oairecerif.author.affiliationMahidol Oxford Tropical Medicine Research Unit
oairecerif.author.affiliationJohnson C. Smith University
oairecerif.author.affiliationCommonwealth Scientific and Industrial Research Organisation
oairecerif.author.affiliationUniversity of Melbourne
oairecerif.author.affiliationNuffield Department of Medicine
oairecerif.author.affiliationTelethon Kids Institute

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