Adaptive Geospatial Surveillance System for Antimalarial Drug Resistance

Disease surveillance activities are usually resource-constrained and should be optimised to achieve spatial and real-time situational awareness. Such optimisation would help with better resource allocation, reduced logistics, and other costs. India has a high population density, diverse geography and climatic conditions, and difficult terrain. With respect to malaria, Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) are endemic, with substantial variability of transmission across the country. While for Pv, drug efficacy appears to be homogenous within the country, for Pf malaria, the resistance pattern varies from the northeastern region to the central region. These factors make accurate mapping of antimalarial drug resistance difficult. To account for these complexities, we develop a targeted and adaptive methodology to identify prospective study sites for Pf antimalarial drug resistance surveillance. We retrieve existing data on the prevalence of validated markers of resistance to Artesunate (AS) and Sulfadoxine-Pyrimethamine (SP) from the World Wide Antimalarial Resistance Network (WWARN) systematic review database. We incorporate these data into a geostatistical model to estimate the prevalence of these markers across India and identify areas with high projected median resistance marker prevalence and low uncertainty. Finally, we create an interactive dashboard using the RShiny software package to simplify the process of selecting sites for future molecular surveillance. This methodology helps to ensure that decision-making is supported by data and modelling outputs while facilitating the generation of knowledge about the current state of antimalarial drug resistance with wide geographic coverage. We demonstrate the utility of our method by selecting sites for surveillance of drug resistance in Pf malaria in India.