You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Integrating Phylodynamics and Epidemiology to Estimate Transmission Diversity in Viral Epidemics
|
|---|---|
| Published in |
PLoS Computational Biology, January 2013
|
| DOI | 10.1371/journal.pcbi.1002876 |
| Pubmed ID | |
| Authors |
Gkikas Magiorkinis, Vana Sypsa, Emmanouil Magiorkinis, Dimitrios Paraskevis, Antigoni Katsoulidou, Robert Belshaw, Christophe Fraser, Oliver George Pybus, Angelos Hatzakis |
| Abstract |
The epidemiology of chronic viral infections, such as those caused by Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV), is affected by the risk group structure of the infected population. Risk groups are defined by each of their members having acquired infection through a specific behavior. However, risk group definitions say little about the transmission potential of each infected individual. Variation in the number of secondary infections is extremely difficult to estimate for HCV and HIV but crucial in the design of efficient control interventions. Here we describe a novel method that combines epidemiological and population genetic approaches to estimate the variation in transmissibility of rapidly-evolving viral epidemics. We evaluate this method using a nationwide HCV epidemic and for the first time co-estimate viral generation times and superspreading events from a combination of molecular and epidemiological data. We anticipate that this integrated approach will form the basis of powerful tools for describing the transmission dynamics of chronic viral diseases, and for evaluating control strategies directed against them. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 2 | 20% |
| France | 1 | 10% |
| Spain | 1 | 10% |
| United States | 1 | 10% |
| Belgium | 1 | 10% |
| Unknown | 4 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 40% |
| Members of the public | 4 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 4% |
| United Kingdom | 5 | 3% |
| Brazil | 2 | 1% |
| Kenya | 1 | <1% |
| Uruguay | 1 | <1% |
| Portugal | 1 | <1% |
| Belgium | 1 | <1% |
| Netherlands | 1 | <1% |
| Venezuela, Bolivarian Republic of | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 150 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 48 | 28% |
| Student > Ph. D. Student | 40 | 23% |
| Student > Master | 17 | 10% |
| Student > Bachelor | 15 | 9% |
| Professor | 9 | 5% |
| Other | 26 | 15% |
| Unknown | 16 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 61 | 36% |
| Medicine and Dentistry | 36 | 21% |
| Biochemistry, Genetics and Molecular Biology | 11 | 6% |
| Mathematics | 9 | 5% |
| Immunology and Microbiology | 7 | 4% |
| Other | 17 | 10% |
| Unknown | 30 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 03 November 2014.
All research outputs
#2,068,245
of 25,901,238 outputs
Outputs from PLoS Computational Biology
#1,801
of 9,068 outputs
Outputs of similar age
#19,421
of 293,151 outputs
Outputs of similar age from PLoS Computational Biology
#23
of 150 outputs
Altmetric has tracked 25,901,238 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,068 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done well, scoring higher than 80% 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 293,151 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.