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.
The approach uses the uncertainty map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling.
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.
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.
Eric PM Grist, Jennifer A Flegg, Georgina Humphreys, Ignacio Suay Mas, Tim JC Anderson, Elizabeth A Ashley, Nicholas PJ Day, Mehul Dhorda, Arjen M Dondorp, M Abul Faiz, Peter W Gething, Tran T Hien, Tin M Hlaing, Mallika Imwong, Jean-Marie Kindermans, Richard J Maude, Mayfong Mayxay, Marina McDew-White, Didier Menard, Shalini Nair, Francois Nosten, Paul N Newton, Ric N Price, Sasithon Pukrittayakamee, Shannon Takala-Harrison, Frank Smithuis, Nhien T Nguyen, Kyaw M Tun, Nicholas J White, et al.. (2016). Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia. International Journal of Health Geographics, 15(1)