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Effectiveness of reactive case detection for malaria elimination in three archetypical transmission settings: a modelling study

Overview of attention for article published in Malaria Journal, June 2017
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  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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Title
Effectiveness of reactive case detection for malaria elimination in three archetypical transmission settings: a modelling study
Published in
Malaria Journal, June 2017
DOI 10.1186/s12936-017-1903-z
Pubmed ID
Authors

Jaline Gerardin, Caitlin A. Bever, Daniel Bridenbecker, Busiku Hamainza, Kafula Silumbe, John M. Miller, Thomas P. Eisele, Philip A. Eckhoff, Edward A. Wenger

Abstract

Reactive case detection could be a powerful tool in malaria elimination, as it selectively targets transmission pockets. However, field operations have yet to demonstrate under which conditions, if any, reactive case detection is best poised to push a region to elimination. This study uses mathematical modelling to assess how baseline transmission intensity and local interconnectedness affect the impact of reactive activities in the context of other possible intervention packages. Communities in Southern Province, Zambia, where elimination operations are currently underway, were used as representatives of three archetypes of malaria transmission: low-transmission, high household density; high-transmission, low household density; and high-transmission, high household density. Transmission at the spatially-connected household level was simulated with a dynamical model of malaria transmission, and local variation in vectorial capacity and intervention coverage were parameterized according to data collected from the area. Various potential intervention packages were imposed on each of the archetypical settings and the resulting likelihoods of elimination by the end of 2020 were compared. Simulations predict that success of elimination campaigns in both low- and high-transmission areas is strongly dependent on stemming the flow of imported infections, underscoring the need for regional-scale strategies capable of reducing transmission concurrently across many connected areas. In historically low-transmission areas, treatment of clinical malaria should form the cornerstone of elimination operations, as most malaria infections in these areas are symptomatic and onward transmission would be mitigated through health system strengthening; reactive case detection has minimal impact in these settings. In historically high-transmission areas, vector control and case management are crucial for limiting outbreak size, and the asymptomatic reservoir must be addressed through reactive case detection or mass drug campaigns. Reactive case detection is recommended only for settings where transmission has recently been reduced rather than all low-transmission settings. This is demonstrated in a modelling framework with strong out-of-sample accuracy across a range of transmission settings while including methodologies for understanding the most resource-effective allocations of health workers. This approach generalizes to providing a platform for planning rational scale-up of health systems based on locally-optimized impact according to simplified stratification.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 126 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 20%
Student > Master 24 19%
Student > Ph. D. Student 16 13%
Student > Postgraduate 7 6%
Student > Doctoral Student 5 4%
Other 15 12%
Unknown 34 27%
Readers by discipline Count As %
Medicine and Dentistry 30 24%
Engineering 8 6%
Social Sciences 7 6%
Agricultural and Biological Sciences 7 6%
Mathematics 5 4%
Other 24 19%
Unknown 45 36%
Attention Score in Context

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 14 June 2017.
All research outputs
#6,631,212
of 24,400,706 outputs
Outputs from Malaria Journal
#1,755
of 5,827 outputs
Outputs of similar age
#99,953
of 321,267 outputs
Outputs of similar age from Malaria Journal
#60
of 124 outputs
Altmetric has tracked 24,400,706 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 5,827 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 69% 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 321,267 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.