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 study uses ‘uncertainty’ map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. It was concluded that 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.
