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Title: Optimal health and disease management using spatial uncertainty: A geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia
Authors: Eric P.M. Grist
Jennifer A. Flegg
Georgina Humphreys
Ignacio Suay Mas
Tim J.C. Anderson
Elizabeth A. Ashley
Nicholas P.J. Day
Mehul Dhorda
Arjen M. Dondorp
M. Abul Faiz
Peter W. Gething
Tran T. Hien
Tin M. Hlaing
Mallika Imwong
Jean Marie Kindermans
Richard J. Maude
Mayfong Mayxay
Marina McDew-White
Didier Menard
Shalini Nair
Francois Nosten
Paul N. Newton
Ric N. Price
Sasithon Pukrittayakamee
Shannon Takala-Harrison
Frank Smithuis
Nhien T. Nguyen
Kyaw M. Tun
Nicholas J. White
Benoit Witkowski
Charles J. Woodrow
Rick M. Fairhurst
Carol Hopkins Sibley
Philippe J. Guerin
WorldWide Antimalarial Resistance Network (WWARN)
Nuffield Department of Clinical Medicine
Monash University
Texas Biomedical Research Institute
Mahidol University
National Institute of Allergy and Infectious Diseases
Dev Care Foundation
University of Oxford
Oxford University Clinical Research Unit
Defence Services Medical Research Centre
Medecins Sans Frontieres, Brussels
Pasteur Institute in Cambodia
Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit (LOMWRU)
University of Maryland School of Medicine
Menzies School of Health Research
Myanmar Oxford Clinical Research Unit
University of Washington, Seattle
Harvard School of Public Health
Keywords: Business, Management and Accounting;Computer Science;Medicine
Issue Date: 24-Oct-2016
Citation: International Journal of Health Geographics. Vol.15, No.1 (2016)
Abstract: © 2016 The Author(s). Background: 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. Methods: The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. Results: The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion. Conclusion: 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.
ISSN: 1476072X
Appears in Collections:Scopus 2016-2017

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