  The coronavirus disease 2019 (COVID-19) has become a public health emergency
of international concern affecting 201 countries and territories around the
globe. As of April 4, 2020, it has caused a pandemic outbreak with more than
11,16,643 confirmed infections and more than 59,170 reported deaths worldwide.
The main focus of this paper is two-fold: (a) generating short term (real-time)
forecasts of the future COVID-19 cases for multiple countries; (b) risk
assessment (in terms of case fatality rate) of the novel COVID-19 for some
profoundly affected countries by finding various important demographic
characteristics of the countries along with some disease characteristics. To
solve the first problem, we presented a hybrid approach based on autoregressive
integrated moving average model and Wavelet-based forecasting model that can
generate short-term (ten days ahead) forecasts of the number of daily confirmed
cases for Canada, France, India, South Korea, and the UK. The predictions of
the future outbreak for different countries will be useful for the effective
allocation of health care resources and will act as an early-warning system for
government policymakers. In the second problem, we applied an optimal
regression tree algorithm to find essential causal variables that significantly
affect the case fatality rates for different countries. This data-driven
analysis will necessarily provide deep insights into the study of early risk
assessments for 50 immensely affected countries.
