Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns

Abstract

The pandemic caused by the spread of the SARS-CoV-2 virus forced governments around the world to impose lockdowns, which mostly involved restricting non-essential activities. Once the rate of infection is manageable, governments must implement strategies that reverse the negative effects of the lockdowns. A decision support system based on fuzzy theory and multi-criteria decision analysis principles is proposed to investigate the importance of a set of key indicators for post-COVID-19 reopening strategies. This system yields more reliable results because it considers the hesitation and experience of decision makers. By including 16 indicators that are utilized by international organizations for comparing, ranking, or investigating countries, our results suggest that governments and policy makers should focus their efforts on reducing violence, crime and unemployment. The provided methodology illustrates the suitability of decision science tools for tackling complex and unstructured problems, such as the COVID-19 pandemic. Governments, policy makers and stakeholders might find in this work scientific-based guidelines that facilitate complex decision-making processes.

Keywords: Analytic hierarchy process; COVID-19; Decision support system; Interval valued intuitionistic fuzzy sets; Lockdowns.

Fig. 1

Membership, non-membership, and hesitancy relations

Fig. 2

Flowchart of the methodology

Fig. 3

The hierarchical model of the indicators that should be considered for a post-COVID-19 lockdown reopening strategy