Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Grist, Eric; Flegg, Jennifer; Humphreys, Georgina; Mas, Ignacio; Anderson, Tim; Ashley, Elizabeth; Day, Nicholas; Dhorda, Mehul; Dondorp, Arjen; Faiz, M.; Gething, Peter; Hien, Tran; Hlaing, Tin; Imwong, Mallika; Kindermans, Jean-Marie; Maude, Richard; Mayxay, Mayfong; McDew-White, Marina; Menard, Didier; Nair, Shalini; Nosten, Francois; Newton, Paul; Price, Ric; Pukrittayakamee, Sasithon; Takala-Harrison, Shannon; Smithuis, Frank; Nguyen, Nhien; Tun, Kyaw; White, Nicholas; Witkowski, Benoit; Woodrow, Charles; Fairhurst, Rick; Sibley, Carol; Guerin, Philippe;
• Is Part Of: International journal of health geographics. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Surveillance -- Drug resistance -- Malaria -- Artemisinin -- Greater Mekong Subregion; Geographic information systems -- Health aspects -- Periodicals; Geography -- Health aspects -- Periodicals; 614.40285
• DOI: 10.1186/s12942-016-0064-6
• Languages: English
• ISSNs: 1476-072X

Abstract

Abstract Background Artemisinin-resistantPlasmodium 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 theK13 -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.