Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction
James Fong, Simon
Herrera Viedma, Enrique
Monte Carlo simulation
Decision support
COVID-19
2019-nCoV
Coronavirus
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. In
computer science, this represents a typical problem of machine learning over incomplete or limited
data in early epidemic 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.
2020-12-17T11:00:56Z
2020-12-17T11:00:56Z
2020
info:eu-repo/semantics/article
Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Composite monte carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing, 93, 106282. doi:10.1016/j.asoc.2020.106282
http://hdl.handle.net/10481/64976
10.1016/j.asoc.2020.106282
eng
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
Atribución-NoComercial-SinDerivadas 3.0 España
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