| Title |
COIL: a methodology for evaluating malarial complexity of infection using likelihood from single nucleotide polymorphism data
|
|---|---|
| Published in |
Malaria Journal, January 2015
|
| DOI | 10.1186/1475-2875-14-4 |
| Pubmed ID | |
| Authors |
Kevin Galinsky, Clarissa Valim, Arielle Salmier, Benoit de Thoisy, Lise Musset, Eric Legrand, Aubrey Faust, Mary Lynn Baniecki, Daouda Ndiaye, Rachel F Daniels, Daniel L Hartl, Pardis C Sabeti, Dyann F Wirth, Sarah K Volkman, Daniel E Neafsey |
| Abstract |
Complex malaria infections are defined as those containing more than one genetically distinct lineage of Plasmodium parasite. Complexity of infection (COI) is a useful parameter to estimate from patient blood samples because it is associated with clinical outcome, epidemiology and disease transmission rate. This manuscript describes a method for estimating COI using likelihood, called COIL, from a panel of bi-allelic genotyping assays. |
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 % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Colombia | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 3% |
| Unknown | 95 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 21% |
| Researcher | 16 | 16% |
| Student > Master | 15 | 15% |
| Student > Postgraduate | 10 | 10% |
| Other | 7 | 7% |
| Other | 14 | 14% |
| Unknown | 15 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 33 | 34% |
| Biochemistry, Genetics and Molecular Biology | 19 | 19% |
| Medicine and Dentistry | 9 | 9% |
| Immunology and Microbiology | 4 | 4% |
| Computer Science | 3 | 3% |
| Other | 11 | 11% |
| Unknown | 19 | 19% |
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 27 March 2015.
All research outputs
#13,325,297
of 22,786,087 outputs
Outputs from Malaria Journal
#3,447
of 5,559 outputs
Outputs of similar age
#171,646
of 352,425 outputs
Outputs of similar age from Malaria Journal
#53
of 115 outputs
Altmetric has tracked 22,786,087 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,559 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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 352,425 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 51% of its contemporaries.
We're also able to compare this research output to 115 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 53% of its contemporaries.