Modeling the spread of COVID-19 in spatio-temporal context

被引：1

作者：

Indika, S. H. Sathish ^{[1
]}

Diawara, Norou ^{[2
]}

Jeng, Hueiwang Anna ^{[3
]}

Giles, Bridget D. ^{[4
]}

Gamage, Dilini S. K. ^{[2
]}

机构：

[1] Virginia Peninsula Community Coll, Dept Math, Hampton, VA 23666 USA

[2] Old Dominion Univ, Dept Math & Stat, Norfolk, VA 23529 USA

[3] Old Dominion Univ, Sch Community & Environm Hlth, Norfolk, VA 23529 USA

[4] Old Dominion Univ, Virginia Modeling Anal & Simulat Ctr, Hampton Rd Biomed Res Consortium Res, Suffolk, VA 23435 USA

来源：

MATHEMATICAL BIOSCIENCES AND ENGINEERING | 2023年 / 20卷 / 06期

关键词：

COVID-19; conditional autoregressive model; Bayesian analysis; Moran statistics; dynamics;

D O I：

10.3934/mbe.2023466

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This study aims to use data provided by the Virginia Department of Public Health to illustrate the changes in trends of the total cases in COVID-19 since they were first recorded in the state. Each of the 93 counties in the state has its COVID-19 dashboard to help inform decision makers and the public of spatial and temporal counts of total cases. Our analysis shows the differences in the relative spread between the counties and compares the evolution in time using Bayesian conditional autoregressive framework. The models are built under the Markov Chain Monte Carlo method and Moran spatial correlations. In addition, Moran's time series modeling techniques were applied to understand the incidence rates. The findings discussed may serve as a template for other studies of similar nature.

引用

页码：10552 / 10569

页数：18

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