Crisis event, foot-and-mouth disease (FMD), epidemic control, real options, decision flexibility, multi-level hierarchic Markov process (MLHMP), uncertainty, decision-support framework, turning moment, dynamic programming, Bayesian forecasting, dynamic models, overreacting, underreacting
This research introduced the real options way of thinking into decision-making in crisis events like animal epidemics, with foot-and-mouth disease (FMD) as a case in point. A unique angle was taken to investigate decision flexibility in choosing optimal control strategies. The main objective was to develop a flexible decision-support framework which corresponds to practice and provides consistent treatment of ongoing uncertainty in controlling animal epidemics. Conceptualisation and operationalisation of decision flexibility were the two main focuses.
A decision analysis revealed the dynamic and sequential nature of decision- making in the control of animal epidemics. The importance of decision flexibility was attributed to the existence of uncertainty and linked decisions in the multi-stage decision process. Timing of control options and the possibility of learning were found to be essential in conceptualising decision flexibility. To operationalise decision flexibility, the main methodological approach was the integration of multi-level hierarchic Markov process (MLHMP) and Bayesian forecasting methods. Based on MLHMP and dynamic generalised linear models (DGLM), a new decision-support framework was developed to investigate the impact of uncertainty and the possibility of learning in choosing the optimal timing of control options over time. The framework paid special attention to the interdependency among strategic, tactical, and operational decisions in managing FMD epidemics. The decision-support framework was shown to be useful in contingency planning for future epidemics.
Addressing the decision flexibility in a dynamic decision process, real options analysis and MLHMP were found to be complementary in developing the flexible decision-support framework. Both required dynamic assessment of future epidemic development and control options. Towards empirical application of the decision-support framework, an integrated epidemic-economic modelling approach was described and illustrated with simulated epidemics. It was shown that, by including decision flexibility in the dynamic decision process of epidemic control, the new modelling approach enabled more realistic estimation of the costs of underreacting or overreacting than the traditional static approaches.