Efficacy of early warning systems in assessing country-level risk exposure to COVID-19

Abstract

COVID-19 has evolved as a pandemic causing unprecedented damages and disruptions to all spheres of life including healthcare, transportation, supply chains, education, and economy, among others. Pandemics are very low-probability events associated with deep uncertainty about the timing of such events and ensuing damages. National policy-makers generally rely on a set of risk indices associated with natural disasters and pandemics to assess the country’s vulnerability and strategy formulation for such rare events. This paper explores the efficacy of early warning systems (disasters and epidemics-based risk ratings) in predicting the country-level exposure to COVID-19. Utilizing three real data-sets reflecting the risk exposure of individual countries to disasters, epidemics, and COVID-19, we explore relations among the associated risk dimensions, namely hazard and exposure, vulnerability, and lack of coping capacity. A comprehensive methodology integrating Pearson’s correlation, ANOVA, and Bayesian Belief Networks-based techniques is adopted to explore and triangulate relations among the three risk indices. Results show that the risk ratings associated with epidemic risk and COVID-19 risk are statistically strongly correlated. However, only the vulnerability dimension of epidemic risk significantly influences the two risks.

Keywords

• covid-19 risk
• bayesian belief networks
• disasters
• epidemics
• pandemic
• healthcare
• vulnerability
• anova