| Title |
A Framework for Modeling Emerging Diseases to Inform Management - Volume 23, Number 1—January 2017 - Emerging Infectious Diseases journal - CDC
|
|---|---|
| Published in |
Emerging Infectious Diseases, January 2017
|
| DOI | 10.3201/eid2301.161452 |
| Pubmed ID | |
| Authors |
Robin E. Russell, Rachel A. Katz, Katherine L.D. Richgels, Daniel P. Walsh, Evan H.C. Grant |
| Abstract |
The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge. |
X Demographics
The data shown below were collected from the profiles of 24 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 | 10 | 42% |
| Croatia | 1 | 4% |
| Unknown | 13 | 54% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 67% |
| Scientists | 6 | 25% |
| Science communicators (journalists, bloggers, editors) | 2 | 8% |
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% |
| Unknown | 94 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 22% |
| Student > Ph. D. Student | 20 | 21% |
| Student > Master | 14 | 15% |
| Student > Doctoral Student | 10 | 10% |
| Student > Postgraduate | 7 | 7% |
| Other | 12 | 13% |
| Unknown | 12 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 36% |
| Environmental Science | 13 | 14% |
| Medicine and Dentistry | 9 | 9% |
| Veterinary Science and Veterinary Medicine | 5 | 5% |
| Nursing and Health Professions | 2 | 2% |
| Other | 15 | 16% |
| Unknown | 17 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 49. 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 21 January 2018.
All research outputs
#836,329
of 25,019,915 outputs
Outputs from Emerging Infectious Diseases
#987
of 9,632 outputs
Outputs of similar age
#17,915
of 432,044 outputs
Outputs of similar age from Emerging Infectious Diseases
#11
of 132 outputs
Altmetric has tracked 25,019,915 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,632 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 45.5. This one has done well, scoring higher than 89% of its peers.
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We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.