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

S. H. Sathish Indika; Norou Diawara; Hueiwang Anna Jeng; Bridget D. Giles; Dilini S. K. Gamage; S. H. Sathish Indika; Norou Diawara; Hueiwang Anna Jeng; Bridget D. Giles; Dilini S. K. Gamage

doi:10.3934/mbe.2023466

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10552-10569. doi: 10.3934/mbe.2023466

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

1.
Department of Mathematics, Virginia Peninsula Community College, Hampton, VA 23666, USA
2.
Department of Mathematics & Statistics, Old Dominion University, Norfolk, VA 23529, USA
3.
School of Community & Environmental Health, Old Dominion University, Norfolk, VA 23529, USA
4.
Hampton Roads Biomedical Research Consortium Research, Virginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA 23435, USA

Academic Editor: Pierre Magal

Received: 23 January 2023 Revised: 10 March 2023 Accepted: 21 March 2023 Published: 11 April 2023

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.
- COVID-19,
- conditional autoregressive model,
- Bayesian analysis,
- Moran statistics,
- dynamics
Citation: S. H. Sathish Indika, Norou Diawara, Hueiwang Anna Jeng, Bridget D. Giles, Dilini S. K. Gamage. Modeling the spread of COVID-19 in spatio-temporal context[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10552-10569. doi: 10.3934/mbe.2023466

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Abstract

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.

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