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Mapping hotspots of malaria transmission from pre-existing hydrology, geology and geomorphology data in the pre-elimination context of Zanzibar, United Republic of Tanzania

Overview of attention for article published in Parasites & Vectors, January 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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11 X users

Citations

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40 Dimensions

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176 Mendeley
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Title
Mapping hotspots of malaria transmission from pre-existing hydrology, geology and geomorphology data in the pre-elimination context of Zanzibar, United Republic of Tanzania
Published in
Parasites & Vectors, January 2015
DOI 10.1186/s13071-015-0652-5
Pubmed ID
Authors

Andrew Hardy, Zawadi Mageni, Stefan Dongus, Gerry Killeen, Mark G Macklin, Silas Majambare, Abdullah Ali, Mwinyi Msellem, Abdul-Wahiyd Al-Mafazy, Mark Smith, Chris Thomas

Abstract

BackgroundLarval source management strategies can play an important role in malaria elimination programmes, especially for tackling outdoor biting species and for eliminating parasite and vector populations when they are most vulnerable during the dry season. Effective larval source management requires tools for identifying geographic foci of vector proliferation and malaria transmission where these efforts may be concentrated. Previous studies have relied on surface topographic wetness to indicate hydrological potential for vector breeding sites, but this is unsuitable for karst (limestone) landscapes such as Zanzibar where water flow, especially in the dry season, is subterranean and not controlled by surface topography.MethodsWe examine the relationship between dry and wet season spatial patterns of diagnostic positivity rates of malaria infection amongst patients reporting to health facilities on Unguja, Zanzibar, with the physical geography of the island, including land cover, elevation, slope angle, hydrology, geology and geomorphology in order to identify transmission hot spots using Boosted Regression Trees (BRT) analysis.ResultsThe distribution of both wet and dry season malaria infection rates can be predicted using freely available static data, such as elevation and geology. Specifically, high infection rates in the central and southeast regions of the island coincide with outcrops of hard dense limestone which cause locally elevated water tables and the location of dolines (shallow depressions plugged with fine-grained material promoting the persistence of shallow water bodies).ConclusionsThis analysis provides a tractable tool for the identification of malaria hotspots which incorporates subterranean hydrology, which can be used to target larval source management strategies.

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X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 176 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Tanzania, United Republic of 2 1%
United Kingdom 1 <1%
Australia 1 <1%
Brazil 1 <1%
Unknown 171 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 38 22%
Student > Ph. D. Student 30 17%
Researcher 28 16%
Student > Bachelor 15 9%
Student > Doctoral Student 6 3%
Other 23 13%
Unknown 36 20%
Readers by discipline Count As %
Medicine and Dentistry 27 15%
Environmental Science 24 14%
Agricultural and Biological Sciences 17 10%
Earth and Planetary Sciences 13 7%
Nursing and Health Professions 10 6%
Other 43 24%
Unknown 42 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 26 February 2015.
All research outputs
#4,755,065
of 25,374,917 outputs
Outputs from Parasites & Vectors
#1,037
of 5,988 outputs
Outputs of similar age
#61,882
of 359,659 outputs
Outputs of similar age from Parasites & Vectors
#20
of 156 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,988 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done well, scoring higher than 82% of its peers.
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 359,659 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 156 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.