Bayesian Hidden Markov Modelling of Blood Type Distribution for Covid-19 Cases Using Poisson Distribution


  •  Johnson Joseph Kwabina Arhinful    
  •  Okyere Gabriel Asare    
  •  Adebanji Atinuke Olusola    
  •  Owusu -Ansah Emmanuel Degraft Johnson    
  •  Burnett Tetteh Accam    

Abstract

This paper proposes a model to describe the blood types distribution of new Coronavirus (COVID-19) cases using the Bayesian Poisson - Hidden Markov Model (BP-HMM). With the help of the Gibbs sampler algorithm, using OpenBugs, the study first identifies the number of hidden states fitting European (EU) and African (AF) data sets of COVID-19 cases by blood type frequency. The study then compares the state-dependent mean of infection within and across the two geographical areas. The study findings show that the number of hidden states and infection rate within and across the two geographical areas differ according to blood type. 



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-7032
  • ISSN(Online): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

Journal Metrics

  • h-index (December 2021): 20
  • i10-index (December 2021): 51
  • h5-index (December 2021): N/A
  • h5-median(December 2021): N/A

( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )

Index

Contact