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Assessing real-time Zika risk in the United States

Overview of attention for article published in BMC Infectious Diseases, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 4,648)
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

news
10 news outlets
blogs
1 blog
twitter
31 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
56 Mendeley
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Title
Assessing real-time Zika risk in the United States
Published in
BMC Infectious Diseases, May 2017
DOI 10.1186/s12879-017-2394-9
Pubmed ID
Authors

Lauren A. Castro, Spencer J. Fox, Xi Chen, Kai Liu, Steven E. Bellan, Nedialko B. Dimitrov, Alison P. Galvani, Lauren Ancel Meyers

Abstract

Confirmed local transmission of Zika Virus (ZIKV) in Texas and Florida have heightened the need for early and accurate indicators of self-sustaining transmission in high risk areas across the southern United States. Given ZIKV's low reporting rates and the geographic variability in suitable conditions, a cluster of reported cases may reflect diverse scenarios, ranging from independent introductions to a self-sustaining local epidemic. We present a quantitative framework for real-time ZIKV risk assessment that captures uncertainty in case reporting, importations, and vector-human transmission dynamics. We assessed county-level risk throughout Texas, as of summer 2016, and found that importation risk was concentrated in large metropolitan regions, while sustained ZIKV transmission risk is concentrated in the southeastern counties including the Houston metropolitan region and the Texas-Mexico border (where the sole autochthonous cases have occurred in 2016). We found that counties most likely to detect cases are not necessarily the most likely to experience epidemics, and used our framework to identify triggers to signal the start of an epidemic based on a policymakers propensity for risk. This framework can inform the strategic timing and spatial allocation of public health resources to combat ZIKV throughout the US, and highlights the need to develop methods to obtain reliable estimates of key epidemiological parameters.

Twitter Demographics

The data shown below were collected from the profiles of 31 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 55 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 27%
Student > Master 9 16%
Student > Ph. D. Student 9 16%
Unspecified 7 13%
Other 4 7%
Other 12 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 25%
Unspecified 9 16%
Social Sciences 5 9%
Pharmacology, Toxicology and Pharmaceutical Science 5 9%
Environmental Science 3 5%
Other 20 36%

Attention Score in Context

This research output has an Altmetric Attention Score of 102. 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 05 February 2018.
All research outputs
#134,981
of 12,465,602 outputs
Outputs from BMC Infectious Diseases
#14
of 4,648 outputs
Outputs of similar age
#6,498
of 260,002 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
Altmetric has tracked 12,465,602 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,648 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 99% 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 260,002 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 97% 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