To control disease outbreaks, critical decisions are necessary in the face of uncertainty. Though we can use models for decision support, key uncertainties about any specific epidemic, in both agricultural and human health settings, cannot be resolved a priori. By monitoring the response of an outbreak to management interventions, one can learn about both model structure and parameter values, to inform decisions. Such evaluation and assessment of competing models is often done in retrospect, and is rarely of use to real-time policies. Rather than focusing on identification of a "best model", Adaptive Management (AM) combines real-time model fitting, based on dynamic surveillance data, with stochastic optimization to select the best management action to maximize management objectives conditional on the current support for competing models. The fundamental innovation of AM is the incorporation of active learning, whereby management actions are evaluated based on their inherent benefit to achieving the objective, as well as their contribution to resolving uncertainties that limit the selection of the best action for the outbreak at hand. Though previously applied in natural resource management, AM has not been generalized for dealing with the management of infectious disease dynamics. Here we propose a multi-year effort to develop an infrastructure for model-based structured decision-making using AM for epidemic response. To demonstrate the feedback between modeling and decision-making, we propose to develop a retrospective analysis of the 2001 UK foot and mouth disease (FMD) epidemic. Through interactions with agency stake-holders in annual workshops, we will develop specific FMD model scenarios to study the interaction of uncertainties in spatial dynamics with decision-making and FMD outbreak response in the US setting. We will develop methods and software to study the FMD case study, which we will employ more generally to investigate AM of other livestock and human outbreaks in the face of various sources of spatial and logistical uncertainties that limit management. Using theoretical models, we will study the application of real-time surveillance data to resolve key uncertainties in spatial locations, transmission networks, and competing local and global objectives for the development of adaptive strategies that can optimally respond to specific outbreak settings.
Advanced planning for outbreak response is limited by our prior knowledge of the epidemic system. Monitoring and evaluation during response to public health emergencies can both improve predictive models and facilitate rapid deployment of optimal interventions for the crisis at hand. We propose to develop novel methods and training, combining decision theory and dynamic epidemic modeling, to facilitate real-time implementation, evaluation, and improvement of public health interventions.
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