The Markov Chains to Predict Malaria Incidence and Death in Gazira State, Sudan From 2001 to 2021

Abstract

Background: Malaria is considered the most deadly and difficult parasitic disease in the world. This study aims to use Markov chains to predict the probability patterns of stability or change in malaria incidence and deaths. Methods: Markov chains were used to analyze the data on malaria incidence and deaths through the Windows Quantitative Systems for Business (WINQSB) program. Data was obtained from the Ministry of Health, Gazira State, Health Information Centre, Sudan. The data is a time series, from 2001 to 2021 per year, according to three cases of decrease, stability, and increase. A transitional matrix is built for the three cases. Results: The results revealed that the probability that malaria incidence and deaths will reach a stable state in one year and in the long run; the probability of transitioning to an increased state was 0.66 of malaria incidence; and the probability of moving to a decreased state was 0.52 of malaria deaths. Conclusion: The results show that the malaria incidence will increase and malaria deaths will decrease in the short and long run from 2022 to 2030 in Gazira State. It is necessary to reinforce means and resources for case management and to investigate the determinants of the situation. Thus, strategies are urgently needed to arrest the unacceptably high incidence and death rates. Keywords: Markov Chain, Predicting, Malaria Incidence, Malaria Death, Gazira State.