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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction
|
|---|---|
| Published in |
Frontiers in Public Health, October 2021
|
| DOI | 10.3389/fpubh.2021.744100 |
| Pubmed ID | |
| Authors |
R. Lakshmana Kumar, Firoz Khan, Sadia Din, Shahab S. Band, Amir Mosavi, Ebuka Ibeke |
X Demographics
The data shown below were collected from the profiles of 4 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 Kingdom | 1 | 25% |
| Switzerland | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 77 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 16% |
| Researcher | 6 | 8% |
| Student > Ph. D. Student | 6 | 8% |
| Student > Bachelor | 5 | 6% |
| Student > Doctoral Student | 2 | 3% |
| Other | 7 | 9% |
| Unknown | 39 | 51% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 18% |
| Medicine and Dentistry | 6 | 8% |
| Engineering | 4 | 5% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Mathematics | 2 | 3% |
| Other | 8 | 10% |
| Unknown | 41 | 53% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 21 October 2021.
All research outputs
#16,889,961
of 24,833,726 outputs
Outputs from Frontiers in Public Health
#5,860
of 13,151 outputs
Outputs of similar age
#255,973
of 427,260 outputs
Outputs of similar age from Frontiers in Public Health
#264
of 560 outputs
Altmetric has tracked 24,833,726 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,151 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has gotten more attention than average, scoring higher than 51% 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 427,260 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 560 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 50% of its contemporaries.