Modeling the Novel Coronavirus Disease (COVID-19) as Hidden Markov Chains

Open Access

Year : 2022 | Volume : | Issue : 2 | Page : 27-35

    Nkemnole E.B.

  1. Udoh N.E.

  1. Professor, Department of Statistics, University of Lagos, Lagos, Nigeria
  2. Research Scholar, Department of Statistics, University of Lagos, Lagos, Nigeria


The Hidden Markov Model has over the years been an appropriate method for modeling diseases such as HIV AIDS, heart failure, and so on. With the ongoing pandemic affecting Nigeria by being responsible for 2,745 death cases out of 207,616 confirmed cases as of October 10, 2021, the need to study an important parameter of COVID-19, the case fatality rate by building a model and estimating the case fatality rate of COVID-19 in Nigeria using Lagos State as case study via hidden Markov approach arose. This study built the transition and emission probabilities of cases of COVID-19 and developed a hidden Markov Model for understanding the trend of the virus. The relevant data were gathered from the website of NCDC (Nigeria Centre for Disease Control). The Forward Algorithm, Baum–Welch, and Viterbi Algorithm were used to implement and proffer answers to the study. The result shows that the estimate case fatality rate of COVID-19 in Lagos State between the periods under study was 4.35%. The likelihood of transitioning from a state of being infected to that of recovery is 25% and the probability of transitioning from a state of being infected to death 50% and the probability of still being infected is 25%. The result of the analysis can be valuable to Governments to design the required interventions in a controlled manner.

Keywords: Markov model, Hidden Markov Model, COVID-19, case fatality rate, HIV, AIDS

[This article belongs to Research & Reviews : Journal of Statistics(rrjs)]

How to cite this article: Nkemnole E.B., Udoh N.E. Modeling the Novel Coronavirus Disease (COVID-19) as Hidden Markov Chains rrjs 2022; 11:27-35
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Regular Issue Open Access Article
Volume 11
Issue 2
Received August 22, 2022
Accepted September 3, 2022
Published September 17, 2022