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A Multi-detection Assay for Malaria Transmitting Mosquitoes
Published on: February 28, 2015
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Model-guided geospatial surveillance system for antimalarial drug resistance
Apoorv Gupta1, Lucinda E Harrison2,3,4,5, Minu Nain1
1ICMR-National Institute of Malaria Research, New Delhi, India.
PLOS Global Public Health
|January 6, 2026
Summary
This study introduces a geospatial framework to optimize malaria surveillance in India. It identifies key study sites for monitoring antimalarial drug resistance in Plasmodium falciparum, ensuring efficient resource allocation.
Area of Science:
- Epidemiology
- Geospatial analysis
- Parasitology
Background:
- Disease surveillance requires optimization due to resource constraints.
- India faces challenges in malaria control due to population density, diverse geography, and variable Plasmodium falciparum drug resistance.
- Existing surveillance methods may not effectively target areas with evolving antimalarial drug resistance.
Purpose of the Study:
- To develop a data-driven decision-making framework for selecting optimal study sites for molecular surveillance of antimalarial drug resistance in Plasmodium falciparum malaria in India.
- To integrate geospatial modeling with existing resistance data to identify high-priority surveillance areas.
- To enhance the efficiency and informativeness of malaria surveillance activities.
Main Methods:
- Data retrieval on antimalarial drug resistance markers from the World Wide Antimalarial Resistance Network (WWARN) Surveyor database.
- Geostatistical modeling to estimate marker prevalence across India, identifying areas of high prevalence and uncertainty.
- Development of an interactive RShiny dashboard to facilitate site selection for molecular surveillance.
Main Results:
- Identification of specific regions in India with high median estimated prevalence and high uncertainty for key antimalarial drug resistance markers.
- Demonstration of a practical framework for selecting molecular surveillance sites based on geospatial modeling outputs.
- Creation of a user-friendly tool (RShiny dashboard) to support operational decision-making in malaria surveillance.
Conclusions:
- The developed geospatial framework effectively guides the selection of sites for molecular surveillance of Plasmodium falciparum malaria.
- Integrating geospatial modeling and existing data enhances the efficiency of resource-constrained disease surveillance.
- This approach supports data-informed operational decisions for effective malaria control in India.
