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In India, heterogeneous malaria transmission and region-specific P. falciparum resistance patterns complicate site selection. This study builds a decision framework using geospatial model outputs: it compiles validated molecular marker data from WWARN, estimates marker prevalence and uncertainty nationwide via geostatistics, and deploys an RShiny dashboard to support operational choices. Results demonstrate site selection for future molecular surveillance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/model-guided-geospatial-surveillance-system-for-antimalarial-drug-resistance/444987/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/model-guided-geospatial-surveillance-system-for-antimalarial-drug-resistance/444987.png","ImageObject",300,407,{"name":92,"@type":93},"jay","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is optimized surveillance important for antimalarial drug resistance monitoring?","Question",{"text":112,"@type":113},"Surveillance resources are limited, so activities must be optimized to generate the most informative findings while using time, finances, and personnel effectively.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data are used to estimate drug resistance marker prevalence across India?",{"text":117,"@type":113},"The framework retrieves existing data on validated resistance markers of artesunate and sulfadoxine-pyrimethamine from the WWARN Surveyor database and incorporates them into a geostatistical model.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the study help choose sites for future molecular surveillance?",{"text":121,"@type":113},"It estimates marker prevalence and uncertainty across India, then provides an interactive RShiny dashboard that simplifies selecting areas with high median estimated marker prevalence and high uncertainty.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},444987,1790811971,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},3985747858866,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","OPEN ACCESS  \nCitation: Gupta A, Harrison LE, Nain M, Phulgenda SS, Chhajed R, Kumar RS, et al.(2026) Model-guided geospatial surveillance system for antimalarial drug resistance. PLOS Glob Public Health 6(1): e0004717. [https://doi](https://doi). org/10.1371/journal.pgph.0004717  \nEditor: Julia Robinson, PLOS: Public Library of Science, UNITED STATES OF AMERICA Received: May 12, 2025  \nAccepted: December 4, 2025  \nPublished: January 6, 2026  \nCopyright: © 2026 Gupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: The source data used for modelling is available from the IDDO/ WWARN Artemisinin Molecular Surveyor ([https://surveyor.iddo.org/map/k13](https://surveyor.iddo.org/map/k13)) and the SP Molecular Surveyor ([https://surveyor.iddo](https://surveyor.iddo). org/map/sp) . R code for the RShiny dashboard  \nRESEARCH ARTICLE  \nModel-guided geospatial surveillance system for antimalarial drug resistance  \nApoorv Gupta1☯, Lucinda E. Harrison2,3,4,5☯, Minu Nain1, Sauman Singh Phulgenda2,4,5, Rutuja Chhajed2,4,5, Roopal S. Kumar1, Aishika Das1, Manju Rahi6,7,  \nPhilippe J. Guerin2,4,5, Anup R. Anvikar1,7, Mehul Dhorda2,4,5,8, Jennifer A. Flegg2,3,4*, Praveen K. Bharti1,7*  \n1 ICMR-National Institute of Malaria Research, New Delhi, India, 2 Infectious Diseases Data Observatory (IDDO), Oxford, United Kingdom, 3 School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia, 4 WorldWide Antimalarial Resistance Network (WWARN), Oxford, United Kingdom, 5 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom, 6 ICMR-Vector Control Research Centre (VCRC), Medical Complex, Puducherry, India, 7 Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India, 8 Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand  \n☯ Contributed equally  \n* [saprapbs@yahoo.co.in](saprapbs@yahoo.co.in) (PKB), [jennifer.flegg@unimelb.edu.au](jennifer.flegg@unimelb.edu.au) (JAF)  \nAbstract  \nDisease 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 sulfadoxinepyrimethamine 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","cbCaiuW8mHmdP0UY","https://ap.wps.com/l/cbCaiuW8mHmdP0UY","pdf",2217697,14,"English","# Abstract\n## Decision-making framework for molecular surveillance\n## Data retrieval and geostatistical modelling\n## Interactive site-selection dashboard\n# Introduction\n## Malaria control goals and treatment background","[{\"question\":\"Why is optimized surveillance important for antimalarial drug resistance monitoring?\",\"answer\":\"Surveillance resources are limited, so activities must be optimized to generate the most informative findings while using time, finances, and personnel effectively.\"},{\"question\":\"What data are used to estimate drug resistance marker prevalence across India?\",\"answer\":\"The framework retrieves existing data on validated resistance markers of artesunate and sulfadoxine-pyrimethamine from the WWARN Surveyor database and incorporates them into a geostatistical model.\"},{\"question\":\"How does the study help choose sites for future molecular surveillance?\",\"answer\":\"It estimates marker prevalence and uncertainty across India, then provides an interactive RShiny dashboard that simplifies selecting areas with high median estimated marker prevalence and high uncertainty.\"}]","Model-guided geospatial surveillance system for antimalarial drug resistance | PDF",1790709988,35]