Grey models for data analysis and decision-making in uncertainty during pandemics

Abstract

We analyse in this study quite a few uncertain decision-making situations concomitant to a pandemic spread and some of the models using grey theory to deal with such situations. We present four stylised models to tackle different decision support situations at the time of any pandemic crisis, such as COVID-19. Eight diverse problems or situations of risk mitigation and decision-making under uncertainties are proposed in this research and the methodologies employing grey incidence analysis, grey clustering, grey prediction, and grey programming models are offered. Numerical illustrations of the applications of these methodologies are also typified in this paper for future development. A practical application of the widely used grey prediction model is also demonstrated in this study to predict the number of COVID-19 infections and fatalities of five Indian states during a considered period of study. The implications of the study are for data scientists, decision-makers, and practitioners to use the benefits of grey theory in dealing with various situations of uncertainty, such as a spread of a pandemic.