A Statistical Framework for the Adaptive Management of Epidemiological Interventions

Abstract

BackgroundEpidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost.MethodologyWe 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.ConclusionsUsing 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.

amei: AnRPackage for the Adaptive Management of Epidemiological Interventions

“…Although amei is based on a very simple cost evaluation scheme, it is straightforward to extend our approach to more complex cost functions, including statedependent costs. We refer the reader to Merl et al (2009) for more detailed discussion of more complex cost assignments.…”

“…To reproduce the results in Merl et al (2009), use MCvits = 100, MCMCpits = 10000, vacsamps = 100, MCreps = 100 and otherwise use the defaults. The object that is returned is of class "MCepi" with fields similar to those that are output from the MCepi function which implements a static (fixed) vaccination strategy.…”

“…It implements the statistical framework introduced by Merl et al (2009). This framework allows one to respond to an emerging epidemic while simultaneously learning about its evolutionary dynamics.…”

“…The mathematical specification of the Bayesian models used for inference and the Monte Carlo methods for constructing optimal vaccination strategies (both static or adaptive and online) are contained in Section 2. In Section 3, the functions and methods implemented in the package are illustrated by following a single, detailed, example, whose results were first reported by Merl et al (2009). Section 4 contains a second example with an alternative transmission model, thereby illustrating the additional benefits of the approach and features of the package.…”

Optimal disease outbreak decisions using stochastic simulation

“…As a result, inference of outbreak state, such as the current number of infecteds, is a significant statistical problem. For the aim of designing intervention policies, sequential inference is necessary and has been studied among others in Dukic, Lopes, and Polson (2010), Merl, Johnson, Gramacy, and Mangel (2009).…”