Atkins, Benjamin D. (2020) Adaptive management of infectious disease epidemics. PhD thesis, University of Warwick.

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Abstract

Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics of spread and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available.

We propose and motivate the use of adaptive management (AM) as a solution to this problem. AM is an iterative, structured decision making framework, encouraging the incorporation of real-time information, resolution of uncertainty and adaptation of control as an outbreak progresses. We investigate in detail how the AM framework can be applied to the management of epidemics. We clarify the effects, benefits and limitations of certain components, such as the difference between active and passive optimisation and the method used to predict uncertainty resolution. We cover a range of scenarios, exhibiting the value of an AM approach in guiding decisions under uncertainty and providing relevant, clear information to decision makers regarding efficient allocation of control and monitoring resources. We believe the practical implementation of such an approach could greatly improve the outcome of epidemics in the future.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Library of Congress Subject Headings (LCSH):	Decision making -- Mathematical models, Communicable diseases -- Prevention, Epidemics -- Government policy, Epidemics -- Prevention
Official Date:	May 2020
Dates:	Date Event May 2020 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Mathematics for Real-World Systems Centre for Doctoral Training
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Tildesley, Mike
Format of File:	pdf
Extent:	vii, 199 leaves : illustrations (some color)
Language:	eng

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