| Title |
Ferrets as Models for Influenza Virus Transmission Studies and Pandemic Risk Assessments - Volume 24, Number 6—June 2018 - Emerging Infectious Diseases journal - CDC
|
|---|---|
| Published in |
Emerging Infectious Diseases, June 2018
|
| DOI | 10.3201/eid2406.172114 |
| Pubmed ID | |
| Authors |
Jessica A. Belser, Wendy Barclay, Ian Barr, Ron A.M. Fouchier, Ryota Matsuyama, Hiroshi Nishiura, Malik Peiris, Charles J. Russell, Kanta Subbarao, Huachen Zhu, Hui-Ling Yen |
| Abstract |
The ferret transmission model is extensively used to assess the pandemic potential of emerging influenza viruses, yet experimental conditions and reported results vary among laboratories. Such variation can be a critical consideration when contextualizing results from independent risk-assessment studies of novel and emerging influenza viruses. To streamline interpretation of data generated in different laboratories, we provide a consensus on experimental parameters that define risk-assessment experiments of influenza virus transmissibility, including disclosure of variables known or suspected to contribute to experimental variability in this model, and advocate adoption of more standardized practices. We also discuss current limitations of the ferret transmission model and highlight continued refinements and advances to this model ongoing in laboratories. Understanding, disclosing, and standardizing the critical parameters of ferret transmission studies will improve the comparability and reproducibility of pandemic influenza risk assessment and increase the statistical power and, perhaps, accuracy of this model. |
X Demographics
The data shown below were collected from the profiles of 18 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 States | 4 | 22% |
| United Kingdom | 1 | 6% |
| Canada | 1 | 6% |
| Switzerland | 1 | 6% |
| Italy | 1 | 6% |
| Unknown | 10 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 72% |
| Scientists | 3 | 17% |
| Practitioners (doctors, other healthcare professionals) | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 63 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 24% |
| Student > Ph. D. Student | 10 | 16% |
| Student > Bachelor | 8 | 13% |
| Student > Master | 6 | 10% |
| Student > Doctoral Student | 4 | 6% |
| Other | 11 | 17% |
| Unknown | 9 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Immunology and Microbiology | 10 | 16% |
| Biochemistry, Genetics and Molecular Biology | 9 | 14% |
| Veterinary Science and Veterinary Medicine | 7 | 11% |
| Medicine and Dentistry | 7 | 11% |
| Agricultural and Biological Sciences | 6 | 10% |
| Other | 11 | 17% |
| Unknown | 13 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 89. 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 10 August 2023.
All research outputs
#473,015
of 25,321,938 outputs
Outputs from Emerging Infectious Diseases
#634
of 9,719 outputs
Outputs of similar age
#10,461
of 337,519 outputs
Outputs of similar age from Emerging Infectious Diseases
#10
of 115 outputs
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