A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak.

Pub Date : 2023-05-29 DOI:10.1007/s42952-023-00210-x

A M Elshehawey, Zhengming Qian

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Abstract

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, $r m^{2} (s^{2} + 2)$ , remarkably lower than $m s^{r m + 1}$ 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.

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一种新的高阶多变量Markov时空分析模型及其在新冠肺炎疫情中的应用。

基于高阶多元马尔可夫链，我们提出了一种新的策略来同时分析随机现象在时间和空间上的演化。我们为由s个可能状态组成的m链建立了一个新的r阶马尔可夫模型，以收集具有现实性的简约性。它可以捕获链之间的负关联和正关联，只需减少参数数量rm2s2+2，显著低于全参数化模型所需的msrm+1。除了应用于分析世界卫生组织（世界卫生组织）地区最近爆发的全球大流行（新冠肺炎）的风险水平的时空动态，以预测流行病学流行的风险状态和监测感染控制外，我们的模型特权还通过蒙特卡洛模拟实验得到了增强。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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