Epidemic outbreaks are extreme events that become less rare and more severe, associated with large social and economic costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks and their contagion. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country-level using objective, time-varying information. We apply both principal component analysis (PCA) and dynamic factor modelling (DFM) to deal with the presence of strong cross-section dependence in the data. 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 index could therefore pro- vide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by firms to assess likely economic consequences of epidemics with useful managerial implications.

Keywords: Risk analysis, Epidemiological risk, Data-driven, Policy framework, Machine learning