A data-driven approach to measuring epidemiological susceptibility risk around the world

Epidemic outbreaks are extreme events that become less rare and more severe. They are associated with large social and economic costs. It is therefore important to evaluate whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying and regularly reproduced information that captures the role of demographics, infrastructure, governance and economic activity conditions. Given the nature of the problem, we choose both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data due to unobserved common factors. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010-2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our framework and index could therefore provide the basis for developing risk assessments of epidemiological risk contagion after the outbreak of an epidemic but also for ongoing monitoring of its spread and social and economic effects. It could be also used by firms to assess likely economic consequences of epidemics with useful managerial implication.