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Modeling the spread of COVID-19 in spatio-temporal context.
Indika, S H Sathish; Diawara, Norou; Jeng, Hueiwang Anna; Giles, Bridget D; Gamage, Dilini S K.
  • Indika SHS; Department of Mathematics, Virginia Peninsula Community College, Hampton, VA 23666, USA.
  • Diawara N; Department of Mathematics & Statistics, Old Dominion University, Norfolk, VA 23529, USA.
  • Jeng HA; School of Community & Environmental Health, Old Dominion University, Norfolk, VA 23529, USA.
  • Giles BD; Hampton Roads Biomedical Research Consortium Research, Virginia Modeling Analysis and Simulation Center, Old Dominion University, Suffolk, VA 23435, USA.
  • Gamage DSK; Department of Mathematics & Statistics, Old Dominion University, Norfolk, VA 23529, USA.
Math Biosci Eng ; 20(6): 10552-10569, 2023 Apr 11.
Article in English | MEDLINE | ID: covidwho-2303152
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Math Biosci Eng Year: 2023 Document Type: Article Affiliation country: Mbe.2023466

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Math Biosci Eng Year: 2023 Document Type: Article Affiliation country: Mbe.2023466