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Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland

Overview of attention for article published in Malaria Journal, February 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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6 tweeters

Citations

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44 Dimensions

Readers on

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91 Mendeley
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1 CiteULike
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Title
Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland
Published in
Malaria Journal, February 2013
DOI 10.1186/1475-2875-12-61
Pubmed ID
Authors

Justin M Cohen, Sabelo Dlamini, Joseph M Novotny, Deepika Kandula, Simon Kunene, Andrew J Tatem

Abstract

ABSTRACT: BACKGROUND: As successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases. METHODS: Household locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season. RESULTS: Results indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012. CONCLUSIONS: The high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 2%
United States 2 2%
United Kingdom 2 2%
Indonesia 1 1%
Cambodia 1 1%
Unknown 83 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 24%
Student > Master 21 23%
Student > Bachelor 11 12%
Student > Ph. D. Student 11 12%
Other 5 5%
Other 14 15%
Unknown 7 8%
Readers by discipline Count As %
Medicine and Dentistry 22 24%
Agricultural and Biological Sciences 20 22%
Environmental Science 8 9%
Computer Science 7 8%
Earth and Planetary Sciences 3 3%
Other 21 23%
Unknown 10 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 23 January 2014.
All research outputs
#1,232,886
of 6,574,878 outputs
Outputs from Malaria Journal
#510
of 2,301 outputs
Outputs of similar age
#64,891
of 295,350 outputs
Outputs of similar age from Malaria Journal
#16
of 74 outputs
Altmetric has tracked 6,574,878 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 2,301 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 77% 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 295,350 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 77% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.