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Predictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya

Overview of attention for article published in PLOS ONE, January 2016
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
policy
2 policy sources
twitter
8 X users

Readers on

mendeley
198 Mendeley
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Article details
Title
Predictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya
Published in
PLOS ONE, January 2016
DOI 10.1371/journal.pone.0144570
Pubmed ID
Authors
Abstract

To-date, Rift Valley fever (RVF) outbreaks have occurred in 38 of the 69 administrative districts in Kenya. Using surveillance records collected between 1951 and 2007, we determined the risk of exposure and outcome of an RVF outbreak, examined the ecological and climatic factors associated with the outbreaks, and used these data to develop an RVF risk map for Kenya. Exposure to RVF was evaluated as the proportion of the total outbreak years that each district was involved in prior epizootics, whereas risk of outcome was assessed as severity of observed disease in humans and animals for each district. A probability-impact weighted score (1 to 9) of the combined exposure and outcome risks was used to classify a district as high (score ≥ 5) or medium (score ≥2 - <5) risk, a classification that was subsequently subjected to expert group analysis for final risk level determination at the division levels (total = 391 divisions). Divisions that never reported RVF disease (score < 2) were classified as low risk. Using data from the 2006/07 RVF outbreak, the predictive risk factors for an RVF outbreak were identified. The predictive probabilities from the model were further used to develop an RVF risk map for Kenya. The final output was a RVF risk map that classified 101 of 391 divisions (26%) located in 21 districts as high risk, and 100 of 391 divisions (26%) located in 35 districts as medium risk and 190 divisions (48%) as low risk, including all 97 divisions in Nyanza and Western provinces. The risk of RVF was positively associated with Normalized Difference Vegetation Index (NDVI), low altitude below 1000m and high precipitation in areas with solonertz, luvisols and vertisols soil types (p <0.05). RVF risk map serves as an important tool for developing and deploying prevention and control measures against the disease.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 198 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 1%
Unknown 196 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 40 20%
Researcher 32 16%
Student > Bachelor 15 8%
Student > Ph. D. Student 15 8%
Student > Doctoral Student 9 5%
Other 30 15%
Unknown 57 29%
Readers by discipline
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 39 20%
Medicine and Dentistry 24 12%
Agricultural and Biological Sciences 24 12%
Immunology and Microbiology 10 5%
Nursing and Health Professions 6 3%
Other 36 18%
Unknown 59 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 01 January 2020.
All research outputs
#1,512,491
of 28,809,062 outputs
Outputs from PLOS ONE
#17,919
of 249,029 outputs
Outputs of similar age
#24,046
of 416,816 outputs
Outputs of similar age from PLOS ONE
#434
of 5,109 outputs
Altmetric has tracked 28,809,062 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 249,029 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 92% 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 416,816 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 5,109 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.