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Elshehawey AM,Qian Zhengming 한국통계학회 2023 Journal of the Korean Statistical Society Vol.52 No.3
We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, rm^2 (s^2 + 2), remarkably lower than ms^(rm+1) required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial–temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.