Designing robust policies under deep uncertainty for mitigating epidemics

Abstract Robust supply chain policies under deep uncertainties are developed for minimizing the impact of an epidemic. A generic integrated supply chain-epidemic model is considered in this paper. Exploratory Modelling and Analysis (EMA) methodology is chosen over predictive modelling in order to find the ensembles of all plausible behaviours of an epidemic. The relative importance of the model parameters is analysed using machine learning algorithms, and scenario discovery analysis is conducted to explore the critical combination of parameters and values responsible for specific scenarios. The analysis revealed that the drug (medicine) shortage and the duration of shortage plays an important role in controlling the dynamics of an epidemic. Finally, a few robust supply chain policies are discussed for mitigating an epidemic.