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Mendeley readers
Attention Score in Context
| Title |
Ebola virus infection modeling and identifiability problems
|
|---|---|
| Published in |
Frontiers in Microbiology, April 2015
|
| DOI | 10.3389/fmicb.2015.00257 |
| Pubmed ID | |
| Authors |
Van Kinh Nguyen, Sebastian C Binder, Alessandro Boianelli, Michael Meyer-Hermann, Esteban A Hernandez-Vargas |
| Abstract |
The recent outbreaks of Ebola virus (EBOV) infections have underlined the impact of the virus as a major threat for human health. Due to the high biosafety classification of EBOV (level 4), basic research is very limited. Therefore, the development of new avenues of thinking to advance quantitative comprehension of the virus and its interaction with the host cells is urgently needed to tackle this lethal disease. Mathematical modeling of the EBOV dynamics can be instrumental to interpret Ebola infection kinetics on quantitative grounds. To the best of our knowledge, a mathematical modeling approach to unravel the interaction between EBOV and the host cells is still missing. In this paper, a mathematical model based on differential equations is used to represent the basic interactions between EBOV and wild-type Vero cells in vitro. Parameter sets that represent infectivity of pathogens are estimated for EBOV infection and compared with influenza virus infection kinetics. The average infecting time of wild-type Vero cells by EBOV is slower than in influenza infection. Simulation results suggest that the slow infecting time of EBOV could be compensated by its efficient replication. This study reveals several identifiability problems and what kind of experiments are necessary to advance the quantification of EBOV infection. A first mathematical approach of EBOV dynamics and the estimation of standard parameters in viral infections kinetics is the key contribution of this work, paving the way for future modeling works on EBOV infection. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Switzerland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Germany | 2 | 2% |
| Unknown | 92 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 19 | 20% |
| Student > Ph. D. Student | 18 | 19% |
| Researcher | 15 | 16% |
| Student > Master | 9 | 9% |
| Student > Doctoral Student | 5 | 5% |
| Other | 20 | 21% |
| Unknown | 10 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 19% |
| Medicine and Dentistry | 16 | 17% |
| Biochemistry, Genetics and Molecular Biology | 9 | 9% |
| Mathematics | 6 | 6% |
| Immunology and Microbiology | 5 | 5% |
| Other | 24 | 25% |
| Unknown | 18 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 29 November 2021.
All research outputs
#15,630,822
of 25,223,158 outputs
Outputs from Frontiers in Microbiology
#13,196
of 28,935 outputs
Outputs of similar age
#142,306
of 271,505 outputs
Outputs of similar age from Frontiers in Microbiology
#168
of 346 outputs
Altmetric has tracked 25,223,158 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 28,935 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has gotten more attention than average, scoring higher than 50% 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 271,505 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 346 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.