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Forecasting Chikungunya spread in the Americas via data-driven empirical approaches

Overview of attention for article published in Parasites & Vectors, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

twitter
12 tweeters

Citations

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15 Dimensions

Readers on

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88 Mendeley
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Title
Forecasting Chikungunya spread in the Americas via data-driven empirical approaches
Published in
Parasites & Vectors, February 2016
DOI 10.1186/s13071-016-1403-y
Pubmed ID
Authors

Luis E. Escobar, Huijie Qiao, A. Townsend Peterson

Abstract

Chikungunya virus (CHIKV) is endemic to Africa and Asia, but the Asian genotype invaded the Americas in 2013. The fast increase of human infections in the American epidemic emphasized the urgency of developing detailed predictions of case numbers and the potential geographic spread of this disease. We developed a simple model incorporating cases generated locally and cases imported from other countries, and forecasted transmission hotspots at the level of countries and at finer scales, in terms of ecological features. By late January 2015, >1.2 M CHIKV cases were reported from the Americas, with country-level prevalences between nil and more than 20 %. In the early stages of the epidemic, exponential growth in case numbers was common; later, however, poor and uneven reporting became more common, in a phenomenon we term "surveillance fatigue." Economic activity of countries was not associated with prevalence, but diverse social factors may be linked to surveillance effort and reporting. Our model predictions were initially quite inaccurate, but improved markedly as more data accumulated within the Americas. The data-driven methodology explored in this study provides an opportunity to generate descriptive and predictive information on spread of emerging diseases in the short-term under simple models based on open-access tools and data that can inform early-warning systems and public health intelligence.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 1 1%
Ecuador 1 1%
Denmark 1 1%
Colombia 1 1%
Poland 1 1%
Unknown 81 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 30%
Professor 10 11%
Student > Ph. D. Student 10 11%
Student > Bachelor 9 10%
Other 7 8%
Other 20 23%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 30%
Medicine and Dentistry 15 17%
Environmental Science 8 9%
Veterinary Science and Veterinary Medicine 6 7%
Mathematics 4 5%
Other 19 22%
Unknown 10 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 08 December 2016.
All research outputs
#2,958,741
of 15,409,340 outputs
Outputs from Parasites & Vectors
#675
of 4,136 outputs
Outputs of similar age
#61,668
of 268,291 outputs
Outputs of similar age from Parasites & Vectors
#1
of 1 outputs
Altmetric has tracked 15,409,340 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,136 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 83% 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 268,291 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 76% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them