| Title |
Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
|
|---|---|
| Published in |
SpringerPlus, January 2016
|
| DOI | 10.1186/s40064-016-1689-4 |
| Pubmed ID | |
| Authors |
Hai-Feng Huo, Xiang-Ming Zhang |
| Abstract |
A more realistic mathematical influenza model including dynamics of Twitter, which may reduce and increase the spread of influenza, is introduced. The basic reproductive number is derived and the stability of the steady states is proved. The existence of Hopf bifurcation are also demonstrated by analyzing the associated characteristic equation. Furthermore, numerical simulations and sensitivity analysis of relevant parameters are also carried out. Our results show that the impact posed by the negative information of Twitter is not significant than the impact posed by the positive information of Twitter on influenza while the impact posed by the negative information of Twitter on the influenza virus is still extraordinary. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 40% |
| United States | 2 | 40% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 8% |
| Unknown | 11 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 50% |
| Student > Bachelor | 2 | 17% |
| Professor | 1 | 8% |
| Student > Master | 1 | 8% |
| Unknown | 2 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 33% |
| Medicine and Dentistry | 2 | 17% |
| Mathematics | 1 | 8% |
| Physics and Astronomy | 1 | 8% |
| Economics, Econometrics and Finance | 1 | 8% |
| Other | 2 | 17% |
| Unknown | 1 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 12 February 2016.
All research outputs
#12,749,139
of 22,844,985 outputs
Outputs from SpringerPlus
#601
of 1,849 outputs
Outputs of similar age
#176,762
of 396,846 outputs
Outputs of similar age from SpringerPlus
#46
of 217 outputs
Altmetric has tracked 22,844,985 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,849 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. 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 396,846 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 55% of its contemporaries.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.