A statistical framework for the adaptive management of epidemiological interventions.
BACKGROUND
Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost.


METHODS
We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates.


CONCLUSIONS
Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptive interventions over non-adaptive ones, in terms of cost and resource efficiency, and robustness to model misspecification.
Merl
Daniel
D
Department of Statistical Science, Duke University, Durham, North Carolina, United States of America. dan@stat.duke.edu
Johnson
Leah R
LR
Gramacy
Robert B
RB
Mangel
Marc
M
D003141:Communicable Diseases; D003198:Computer Simulation; D003661:Decision Support Techniques; D004196:Disease Outbreaks; D004813:Epidemiology / Q000295:instrumentation* / Q000458:organization & administration / Q000592:standards*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology* / Q000628:therapy*; D015233:Models, Statistical; D009010:Monte Carlo Method; D011634:Public Health / Q000379:methods; D014611:Vaccination / Q000379:methods*