A Framework for Modeling Emerging Diseases to Inform Management

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 th...

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Bibliographic Details
Published in:Emerging infectious diseases 2017-01, Vol.23 (1), p.1-6
Main Authors: Russell, Robin E, Katz, Rachel A, Richgels, Katherine L D, Walsh, Daniel P, Grant, Evan H C
Format: Article
Language:English
Subjects:
A Framework for Modeling Emerging Diseases to Inform Management
Animals
Clinical Decision-Making - methods
Communicable Diseases, Emerging - epidemiology
Communicable Diseases, Emerging - prevention & control
Communicable Diseases, Emerging - transmission
decision analysis
Decision Support Techniques
Disease Management
emerging diseases
Humans
infectious diseases
one health
Risk factors
Uncertainty
United States - epidemiology
Zoonoses
Zoonoses - epidemiology
Zoonoses - prevention & control
Zoonoses - transmission
zoonotic diseases
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Summary: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.
ISSN:1080-6040
1080-6059
DOI:10.3201/eid2301.161452