Social resilience and risk society analysis model of the COVID 19 pandemic

Abstract

The 2019 pandemic disrupted established orders in health, economy, and social phenomena, necessitating new approaches to respond. This paper conducts empirical research to identify integrated crisis recognition and response strategies in a risk society exemplified by COVID-19, aiming to lay a foundation for humanity's response to future global crises. The study focuses on analyzing structural changes in the intellectual ecosystem to ensure resilience in crises, using the Kolmogorov–Smirnov test and the bowtie model to assess impact intensity and change patterns. The research found that COVID-19 marked a transition to a new state in the intellectual ecosystem for resilience compared to the Middle East Respiratory Syndrome (MERS) era, with 'viruses and vaccines' research emerging as a key driver of change.Integrating these findings, the study proposes an expanded concept of social resilience within the Social-Ecological-Technical Systems (SETS) framework. Centering on the concept of 'data-driven knowledge resilience’, it advocates a dynamic learning process that reconfigures response strategies and updates disaster management systems. Therefore, this research presents new directions in disaster management that are reactive, predictive, adaptive, and innovative, extending the social resilience framework to include knowledge-driven, learning-focused, and dynamically adaptable approaches.