Resilience Analysis in the Wake of COVID-19: Insights from Bayesian Modeling

The current world has experienced a profound shift from risk analysis to resilience analysis, a transition underscored by the recognition that resilience encompasses more than just a system's response to threats. It also provides critical insights into preparedness for future events and the recovery processes that follow. The recent COVID-19 pandemic has profoundly impacted global societies, illustrating the vulnerabilities within our systems and the need for enhanced resilience. For over three years, communities worldwide faced unprecedented challenges, highlighting the necessity to evaluate the socio-technical resilience of our societies. Understanding how resilient we are against such threats is essential, and ensuring a swift recovery post-event is equally critical.<br>In this paper, we demonstrate the applicability of Bayesian networks in modeling resilience and its various phases with respect to the pandemic. Unlike deep learning methods, which often rely solely on large datasets, Bayesian networks offer the unique advantage of incorporating expert knowledge alongside empirical data. This dual approach allows for a more nuanced understanding of resilience dynamics. We present a data-driven multilevel hierarchical Bayesian network that not only estimates and compares the different phases of resilience but also identifies and analyzes the underlying factors that influence each phase. To assess socio-technical resilience effectively, we utilized a German dataset (INKAR), which contains vital socio-economic indicators, including population, employment, education, and gender, at a community-level geographical resolution. This research aims to contribute to the growing body of knowledge on resilience, providing valuable insights that can inform policy and practice. The results quantify the resilience of single counties and show the coping capacity concerning the pandemic over the past years.