Exploring Probabilistic Network-Based Modeling of Multidimensional Factors Associated with Country Risk

Nexus between drivers of COVID-19 and country risks

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi-dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.

Adoption of a Data-Driven Bayesian Belief Network Investigating Organizational Factors that Influence Patient Safety

Medical errors pose high risks to patients. Several organizational factors may impact the high rate of medical errors in complex and dynamic healthcare systems. However, limited research is available regarding probabilistic interdependencies between the organizational factors and patient safety errors. To explore this, we adopt a data-driven Bayesian Belief Network (BBN) model to represent a class of probabilistic models, using the hospital-level aggregate survey data from U.K. hospitals. Leveraging the use of probabilistic dependence models and visual features in the BBN model, the results shed new light on relationships existing among eight organizational factors and patient safety errors. With the high prediction capability, the data-driven approach results suggest that "health and well-being" and "bullying and harassment in the work environment" are the two leading factors influencing the number of reported errors and near misses affecting patient safety. This study provides significant insights to understand organizational factors' role and their relative importance in supporting decision-making and safety improvements.

Prioritizing Multidimensional Interdependent Factors Influencing COVID-19 Risk

COVID-19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy-makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country-level data-set related to humanitarian crises and disasters to explore critical factors influencing COVID-19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID-19 risk in a probabilistic network setting. This study provides unique insights to policy-makers regarding the identification of critical factors influencing COVID-19 risk and their relative importance in a network setting.