Eric P.M. GristJennifer A. FleggGeorgina HumphreysIgnacio Suay MasTim J.C. AndersonElizabeth A. AshleyNicholas P.J. DayMehul DhordaArjen M. DondorpM. Abul FaizPeter W. GethingTran T. HienTin M. HlaingMallika ImwongJean Marie KindermansRichard J. MaudeMayfong MayxayMarina McDew-WhiteDidier MenardShalini NairFrancois NostenPaul N. NewtonRic N. PriceSasithon PukrittayakameeShannon Takala-HarrisonFrank SmithuisNhien T. NguyenKyaw M. TunNicholas J. WhiteBenoit WitkowskiCharles J. WoodrowRick M. FairhurstCarol Hopkins SibleyPhilippe J. GuerinWorldWide Antimalarial Resistance Network (WWARN)Nuffield Department of Clinical MedicineMonash UniversityTexas Biomedical Research InstituteMahidol UniversityNational Institute of Allergy and Infectious DiseasesDev Care FoundationUniversity of OxfordOxford University Clinical Research UnitDefence Services Medical Research CentreMedecins Sans Frontieres, BrusselsPasteur Institute in CambodiaLao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit (LOMWRU)University of Maryland School of MedicineMenzies School of Health ResearchMyanmar Oxford Clinical Research UnitUniversity of Washington, SeattleHarvard School of Public Health2018-12-112019-03-142018-12-112019-03-142016-10-24International Journal of Health Geographics. Vol.15, No.1 (2016)1476072X2-s2.0-84992360038https://repository.li.mahidol.ac.th/handle/20.500.14594/43233© 2016 The Author(s). Background: Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic 'smart surveillance' methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently. Methods: The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. Results: The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion. Conclusion: This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas.Mahidol UniversityBusiness, Management and AccountingComputer ScienceMedicineArticleSCOPUS10.1186/s12942-016-0064-6