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 changing 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 (FMD) 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.
Results/Conclusions
The recommended optimal culling or vaccination strategy for FMD outbreak response depends critically on the variance in parameter estimates and the current size of the outbreak. Inference for the optimal response strategy based on early outbreak surveillance can identify the appropriate ranking of control options, but will poorly estimate the final outcome. For the 2001 FMD outbreak, forward simulation using parameters estimated from real-time observed data can rapidly (6-8 weeks) identify the preferred intervention and are consistent with recommendations based on parameters estimated using the full outbreak time series. Further, we show that regardless of the parameter uncertainty, the optimal interventions are time dependent; favoring more conservative interventions when the outbreak is large and interventions are near the logistical capacity of a response, and more severe interventions as the outbreak wanes and logistical capacity is not limiting.