Matthew Ferrari

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  Managing multiple sources of uncertainty: optimal outbreak response for Foot-and-Mouth Disease
  Matthew Ferrari

  Control of epizootics require that decisions be made in the face of multiple sources of uncertainty: economic, political and logistical uncertainty, dynamical uncertainty about epizootiological processes, and stochastic nature of disease spread. Decision-makers are faced with fundamental trade-off between the learning that will accrue through continued observation of a disease process and the opportunity cost of inaction. Structured decision-making and adaptive management seek to minimize the opportunity cost of inaction by defining an iterative, state-dependent policy for selecting among alternative management actions. In particular, we seek to define an adaptive policy that responds to the changing state of information about competing dynamical models as defined in the posterior distribution and the chaining epizootiological state as defined by the size and spatial extent of an outbreak. We achieve the former through an analysis of the value of information across competing models and sequential analysis of real-time outbreak surveillance from the 2001 foot-and-mouth outbreak in the UK. We achieve the latter by using reinforcement learning to solve for an optimal state-dependent policy for the application of vaccination and culling for a spatially explicit livestock outbreak. We show that adaptive policies can result in significant gains over conventional static management.