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    Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia

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    Author
    Grist, EPM; Flegg, JA; Humphreys, G; Mas, IS; Anderson, TJC; Ashley, EA; Day, NPJ; Dhorda, M; Dondorp, AM; Abul Faiz, M; ...
    Date
    2016-10-24
    Source Title
    International Journal of Health Geographics
    Publisher
    BMC
    University of Melbourne Author/s
    Flegg, Jennifer
    Affiliation
    School of Mathematics and Statistics
    Metadata
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    Document Type
    Journal Article
    Citations
    Grist, E. P. M., Flegg, J. A., Humphreys, G., Mas, I. S., Anderson, T. J. C., Ashley, E. A., Day, N. P. J., Dhorda, M., Dondorp, A. M., Abul Faiz, M., Gething, P. W., Hien, T. T., Hlaing, T. M., Imwong, M., Kindermans, J. -M., Maude, R. J., Mayxay, M., McDew-White, M., Menard, D. ,... Guerin, P. J. (2016). Optimal health and disease management using spatial uncertainty: a geographic characterization of emergent artemisinin-resistant Plasmodium falciparum distributions in Southeast Asia. INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, 15 (1), https://doi.org/10.1186/s12942-016-0064-6.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256636
    DOI
    10.1186/s12942-016-0064-6
    Abstract
    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.

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