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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks
|
|---|---|
| Published in |
BMC Bioinformatics, August 2014
|
| DOI | 10.1186/1471-2105-15-276 |
| Pubmed ID | |
| Authors |
Michael J Kane, Natalie Price, Matthew Scotch, Peter Rabinowitz |
| Abstract |
Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 29% |
| Norway | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 350 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Portugal | 1 | <1% |
| Unknown | 347 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 64 | 18% |
| Student > Ph. D. Student | 58 | 17% |
| Student > Bachelor | 37 | 11% |
| Researcher | 30 | 9% |
| Student > Doctoral Student | 20 | 6% |
| Other | 50 | 14% |
| Unknown | 91 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 68 | 19% |
| Engineering | 33 | 9% |
| Agricultural and Biological Sciences | 24 | 7% |
| Medicine and Dentistry | 20 | 6% |
| Mathematics | 16 | 5% |
| Other | 86 | 25% |
| Unknown | 103 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 13 January 2022.
All research outputs
#2,527,949
of 25,346,731 outputs
Outputs from BMC Bioinformatics
#644
of 7,676 outputs
Outputs of similar age
#24,582
of 238,118 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 116 outputs
Altmetric has tracked 25,346,731 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,676 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 91% 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 238,118 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 116 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.