  The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened
up new challenges to the research community. Artificial intelligence (AI)
driven methods can be useful to predict the parameters, risks, and effects of
such an epidemic. Such predictions can be helpful to control and prevent the
spread of such diseases. The main challenges of applying AI is the small volume
of data and the uncertain nature. Here, we propose a shallow long short-term
memory (LSTM) based neural network to predict the risk category of a country.
We have used a Bayesian optimization framework to optimize and automatically
design country-specific networks. The results show that the proposed pipeline
outperforms state-of-the-art methods for data of 180 countries and can be a
useful tool for such risk categorization. We have also experimented with the
trend data and weather data combined for the prediction. The outcome shows that
the weather does not have a significant role. The tool can be used to predict
long-duration outbreak of such an epidemic such that we can take preventive
steps earlier
