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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A hybrid seasonal prediction model for tuberculosis incidence in China
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2013
|
| DOI | 10.1186/1472-6947-13-56 |
| Pubmed ID | |
| Authors |
Shiyi Cao, Feng Wang, Wilson Tam, Lap Ah Tse, Jean Hee Kim, Junan Liu, Zuxun Lu |
| Abstract |
Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China. |
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 | 1 | 14% |
| India | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 86% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 73 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 14 | 19% |
| Student > Ph. D. Student | 13 | 18% |
| Student > Bachelor | 6 | 8% |
| Researcher | 6 | 8% |
| Other | 6 | 8% |
| Other | 12 | 16% |
| Unknown | 17 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 24% |
| Nursing and Health Professions | 8 | 11% |
| Computer Science | 5 | 7% |
| Agricultural and Biological Sciences | 4 | 5% |
| Engineering | 4 | 5% |
| Other | 12 | 16% |
| Unknown | 23 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 28 May 2013.
All research outputs
#6,391,729
of 22,708,120 outputs
Outputs from BMC Medical Informatics and Decision Making
#612
of 1,981 outputs
Outputs of similar age
#53,685
of 192,695 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#12
of 34 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,981 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 67% 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 192,695 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.