The COVID-19 pandemic has had a profound impact on global health and has highlighted the importance of understanding the transmission dynamics of infectious diseases. This study aimed to construct a COVID-19 transmission matrix in Kenya using the Markov chain and to examine the equilibrium distribution and steady states for COVID-19 transmission in Kenya. The study utilized data from the Ministry of Health in Kenya and other sources to estimate the transition probabilities used in the Markov chain model. The results showed that the transmission of COVID 19 in Kenya is primarily driven by human mobility and the spread of the virus from infected individuals to susceptible individuals. The equilibrium distribution indicated that the steady state for COVID-19 transmission in Kenya is heavily dependent on the control measures that are in place. The steady states for COVID-19 transmission in Kenya were estimated to be lower for scenarios with more stringent control measures in place. The results of the study showed that the COVID-19 transmission matrix in Kenya is dynamic and influenced by a range of factors, including human behavior, the availability of effective interventions, and the emergence of new variants of the virus. The equilibrium distribution of COVID-19 transmission in Kenya was found to be influenced by the presence of comorbidities, the availability of effective treatments, and the degree of community transmission. The steady states for COVID-19 transmission in Kenya were found to be influenced by the effectiveness of interventions, including the use of masks, social distancing measures, and the availability of vaccines. The results of this study provide important insights into the transmission dynamics of COVID-19 in Kenya, and can inform the development of more effective strategies for controlling its spread. In conclusion, the results of this study demonstrate the utility of Markov chain models for the probabilistic analysis of COVID-19 transmission. The findings of this study highlight the need for continued monitoring of COVID 19 transmission in Kenya, and for the development of effective interventions to control its spread. In conclusion, the probabilistic analysis of COVID-19 transmission in Kenya conducted in this study is an important step towards understanding the transmission dynamics of the virus and towards developing effective control measures. Further research is needed to improve the accuracy of the model and to understand the complex dynamics of COVID-19 transmission in Kenya and other populations.
The COVID-19 pandemic has had a profound impact on global health and has highlighted the importance of understanding the transmission dynamics of infectious diseases. This study aimed to construct a COVID-19 transmission matrix in Kenya using the Markov chain and to examine the equilibrium distribution and steady states for COVID-19 transmission in Kenya. The study utilized data from the Ministry of Health in Kenya and other sources to estimate the transition probabilities used in the Markov chain model. The results showed that the transmission of COVID 19 in Kenya is primarily driven by human mobility and the spread of the virus from infected individuals to susceptible individuals. The equilibrium distribution indicated that the steady state for COVID-19 transmission in Kenya is heavily dependent on the control measures that are in place. The steady states for COVID-19 transmission in Kenya were estimated to be lower for scenarios with more stringent control measures in place. The results of the study showed that the COVID-19 transmission matrix in Kenya is dynamic and influenced by a range of factors, including human behavior, the availability of effective interventions, and the emergence of new variants of the virus. The equilibrium distribution of COVID-19 transmission in Kenya was found to be influenced by the presence of comorbidities, the availability of effective treatments, and the degree of community transmission. The steady states for COVID-19 transmission in Kenya were found to be influenced by the effectiveness of interventions, including the use of masks, social distancing measures, and the availability of vaccines. The results of this study provide important insights into the transmission dynamics of COVID-19 in Kenya, and can inform the development of more effective strategies for controlling its spread. In conclusion, the results of this study demonstrate the utility of Markov chain models for the probabilistic analysis of COVID-19 transmission. The findings of this study highlight the need for continued monitoring of COVID 19 transmission in Kenya, and for the development of effective interventions to control its spread. In conclusion, the probabilistic analysis of COVID-19 transmission in Kenya conducted in this study is an important step towards understanding the transmission dynamics of the virus and towards developing effective control measures. Further research is needed to improve the accuracy of the model and to understand the complex dynamics of COVID-19 transmission in Kenya and other populations.