  In the advent of the novel coronavirus epidemic since December 2019,
governments and authorities have been struggling to make critical decisions
under high uncertainty at their best efforts. Composite Monte-Carlo (CMC)
simulation is a forecasting method which extrapolates available data which are
broken down from multiple correlated/casual micro-data sources into many
possible future outcomes by drawing random samples from some probability
distributions. For instance, the overall trend and propagation of the infested
cases in China are influenced by the temporal-spatial data of the nearby cities
around the Wuhan city (where the virus is originated from), in terms of the
population density, travel mobility, medical resources such as hospital beds
and the timeliness of quarantine control in each city etc. Hence a CMC is
reliable only up to the closeness of the underlying statistical distribution of
a CMC, that is supposed to represent the behaviour of the future events, and
the correctness of the composite data relationships. In this paper, a case
study of using CMC that is enhanced by deep learning network and fuzzy rule
induction for gaining better stochastic insights about the epidemic development
is experimented. Instead of applying simplistic and uniform assumptions for a
MC which is a common practice, a deep learning-based CMC is used in conjunction
of fuzzy rule induction techniques. As a result, decision makers are benefited
from a better fitted MC outputs complemented by min-max rules that foretell
about the extreme ranges of future possibilities with respect to the epidemic.
