Details:

Alternative Title:
Emerg Infect Dis
Publisher's site:
http://dx.doi.org/10.3201/eid2301.161452
Personal Author:
Russell, Robin E. ; Katz, Rachel A. ; Richgels, Katherine L.D. ; Walsh, Daniel P. ; Grant, Evan H.C.
Russell, Robin E. ; Katz, Rachel A. ; Richgels, Katherine L.D. ; Walsh, Daniel P. ; Grant, Evan H.C. Less -
Description:
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.
Subject:
[+]
A Framework For Modeling Emerging Diseases To Inform Management Decision Analysis Emerging Diseases Infectious Diseases Mathematical Modelling Model Development One Health Perspective Predictive Modelling Zoonoses Zoonotic Diseases
Source:
Emerg Infect Dis. 23(1):1-6.
Pubmed ID:
27983501
Pubmed Central ID:
PMC5176225
Document Type:
Journal Article
Collection(s):
Emerging Infectious Diseases
Main Document Checksum:
[+]
urn:sha256:48f611cf711a54a709c311a3bf81bc1fabc9b4c0ca270801ae226fc8117151f7
Download URL:
https://stacks.cdc.gov/view/cdc/43552/cdc_43552_DS1.pdf
File Type:

Supporting Files

• 	16-1452-F.gif	gif
  	16-1452-F.jpg	jpeg
  	16-1452-Techapp-s1.pdf	pdf
  	16-1452.nxml	bin

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