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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

Overview of attention for article published in International Journal of Health Geographics, October 2016
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2 tweeters
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1 Facebook page

Citations

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10 Dimensions

Readers on

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83 Mendeley
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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
Published in
International Journal of Health Geographics, October 2016
DOI 10.1186/s12942-016-0064-6
Pubmed ID
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

Abstract

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. The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 28%
Student > Ph. D. Student 11 13%
Student > Master 10 12%
Student > Bachelor 6 7%
Professor > Associate Professor 5 6%
Other 15 18%
Unknown 13 16%
Readers by discipline Count As %
Medicine and Dentistry 22 27%
Agricultural and Biological Sciences 12 14%
Computer Science 6 7%
Biochemistry, Genetics and Molecular Biology 6 7%
Immunology and Microbiology 5 6%
Other 14 17%
Unknown 18 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 December 2017.
All research outputs
#8,705,062
of 13,893,500 outputs
Outputs from International Journal of Health Geographics
#330
of 501 outputs
Outputs of similar age
#164,553
of 290,115 outputs
Outputs of similar age from International Journal of Health Geographics
#30
of 44 outputs
Altmetric has tracked 13,893,500 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 501 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,115 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.