Model-guided geospatial surveillance system for antimalarial drug resistance

Journal article

Disease surveillance activities are usually resource-constrained and should be optimised to generate the most informative scientific findings, and to make the best use of time, finances, and personnel. India has a high population density, diverse geography and climatic conditions, and difficult terrain. With respect to malaria, Plasmodium falciparum and Plasmodium vivax are endemic, with substantial variability of transmission across the country. While for P. vivax, drug efficacy appears to be homogeneous within the country, for P. falciparum malaria, the drug resistance pattern varies from the northeastern region to the central region. Accounting for these complexities, we develop a decision-making framework guided by geospatial modelling outputs to identify prospective study sites for surveillance of molecular markers of antimalarial drug resistance in P. falciparum malaria in India. We first retrieve existing data on the prevalence of validated markers of resistance to artesunate and sulfadoxine-pyrimethamine from the World Wide Antimalarial Resistance Network (WWARN) Surveyor database. We then incorporate these data into a geostatistical model to estimate the prevalence of these markers across India and identify areas with high median estimated marker prevalence and high uncertainty. Finally, we create an interactive dashboard using the RShiny software package to simplify the process of selecting sites for future molecular surveillance. Our framework helps to ensure that operational decision-making is supported by data and modelling outputs. We demonstrate the utility of our framework by selecting sites for molecular surveillance of P. falciparum malaria in India.

Authors: Gupta, A; Harrison, LE; Nain, M; Phulgenda, SS; Chhajed, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLOS Global Public Health
• Volume: 6
• Issue: 1
• Article number: e0004717
• Publication date: 2026-01-06
• Acceptance date: 2025-12-04
• DOI: 10.1371/journal.pgph.0004717
• EISSN: 2767-3375
• ISSN: 2767-3375
• Language: English