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A Smartphone-Based Application Improves the Accuracy, Completeness, and Timeliness of Cattle Disease Reporting and Surveillance in Ethiopia

Overview of attention for article published in Frontiers in Veterinary Science, January 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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1 blog
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16 X users

Citations

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11 Dimensions

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59 Mendeley
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Title
A Smartphone-Based Application Improves the Accuracy, Completeness, and Timeliness of Cattle Disease Reporting and Surveillance in Ethiopia
Published in
Frontiers in Veterinary Science, January 2018
DOI 10.3389/fvets.2018.00002
Pubmed ID
Authors

Tariku Jibat Beyene, Fentahun Asfaw, Yitbarek Getachew, Takele Beyene Tufa, Iain Collins, Ashenafi Feyisa Beyi, Crawford W. Revie

Abstract

Accurate disease reporting, ideally in near real time, is a prerequisite to detecting disease outbreaks and implementing appropriate measures for their control. This study compared the performance of the traditional paper-based approach to animal disease reporting in Ethiopia to one using an application running on smartphones. In the traditional approach, the total number of cases for each disease or syndrome was aggregated by animal species and reported to each administrative level at monthly intervals; while in the case of the smartphone application demographic information, a detailed list of presenting signs, in addition to the putative disease diagnosis were immediately available to all administrative levels via a Cloud-based server. While the smartphone-based approach resulted in much more timely reporting, there were delays due to limited connectivity; these ranged on average from 2 days (in well-connected areas) up to 13 days (in more rural locations). We outline the challenges that would likely be associated with any widespread rollout of a smartphone-based approach such as the one described in this study but demonstrate that in the long run the approach offers significant benefits in terms of timeliness of disease reporting, improved data integrity and greatly improved animal disease surveillance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 17%
Researcher 10 17%
Student > Ph. D. Student 9 15%
Student > Doctoral Student 4 7%
Lecturer 3 5%
Other 7 12%
Unknown 16 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 20%
Veterinary Science and Veterinary Medicine 11 19%
Computer Science 6 10%
Engineering 4 7%
Medicine and Dentistry 3 5%
Other 7 12%
Unknown 16 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 15 January 2019.
All research outputs
#1,795,044
of 23,016,919 outputs
Outputs from Frontiers in Veterinary Science
#318
of 6,325 outputs
Outputs of similar age
#44,981
of 442,088 outputs
Outputs of similar age from Frontiers in Veterinary Science
#8
of 72 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,325 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 94% 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 442,088 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 89% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.