Sudan Journal of Medical Sciences
ISSN: 1858-5051
High-impact research on the latest developments in medicine and healthcare across MENA and Africa
The Markov Chains to Predict Malaria Incidence and Death in Gazira State, Sudan From 2001 to 2021
Published date: Jun 28 2024
Journal Title: Sudan Journal of Medical Sciences
Issue title: Sudan JMS: Volume 19 (2024), Issue No. 2
Pages: 276–286
Authors:
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.
References:
[1] Voskoglou, M. (2016). Applications of Finite Markov Chain Models to Management. American Journal of Computational and Applied Mathematics, 6(1), 7–13. https://doi: 10.5923/j.ajcam.20160601.02
[2] Nkiruka, O., Prasad, R., & Clement, O. (2021). Prediction of malaria incidence using climate variability and machine learning. Informatics in Medicine Unlocked, 22, 100508.
[3] Akhi, A. A., Kamrujjaman, M., Nipa, K. F., & Khan, T. (2023). A continuous-time Markov chain and stochastic differential equations approach for modeling malaria propagation. Healthcare Analytics, 4, 100239, ISSN 2772-4425, https://doi.org/ 10.1016/j.health.2023.100239
[4] Lubinda, J., Bi, Y., Hamainza, B., Haque, U., & Moore, A. J. (2021). Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden. PLoS Computational Biology. https://doi.org/ 10.1371/journal.pcbi.1008669
[5] Mohammad Fadlelkarim, B. O. (2022). Markov chains for forecasting of probabilities of exchange rate of Sudanese pound against the US Dollar for the period 1999 to 2015. Journal of the Faculty of Economics, 21.
[6] Hamza, S. K., Ahmed, A. D., & Hussein, S. A. (2020). Forecasting the exchange rate of the Iraqi dinar against the US dollar using Markov chains. Periodicals of Engineering and Natural Sciences, 8(2), 626–631. http://pen.ius.edu.ba/index.php/pen/article/view/1280
[7] Hussien, H. H., Eissa, F. H., & Awadalla, K. E. (2017). Statistical methods for predicting malaria incidences using data from Sudan. Malaria Research and Treatment, 2017, 4205957. https://doi.org/10.1155/2017/4205957
[8] Oliver, C. (2009). Markov processes for stochastic modeling (1st ed.). Elsevier Academic Press. http://www.sciencedirect.com/science/book/9780124077959
[9] Rosenblatt, M. (1971). Markov processes. Structure and asymptotic behavior. Springer Berlin. https://doi.org/10.1007/978-3-642-65238-7
[10] Taylor, H. (1998). An Introduction to stochastic modeling (3rd ed.). University Stanford, California. https://appliedmath.arizona.edu/sites/default/files/0f04d86a836182cbf608dfc86c7a70f5e5f6_0.pdf
[11] Mourad, M., & Harb, A. (2013). A predictive model for the daily exchange rate of the EUR/USD using Markov chain and co-integration techniques. Lebanese Science Journal, 14(2), 950–970. https://applications.emro.who.int/imemrf/Lebanese_Sci_J/Lebanese_Sci_J_2013_14_2_93_113.pdf
[12] Madhava Rao, K. S., & Ramachandran, A. (2016). A Markov approach to exchange rate sentiment analysis of major global currencies. Open Journal of Statistics, 6. https://www.scirp.org/journal/paperinformation?paperid=73204
[13] Serfozo, R. (2009). Basics of applied stochastic processes, probability and its applications. Springer-Verlag Berlin. https://books.google.com.sa/books?id=JBBRiuxTN0QC&printsec=frontcover&hl=ar&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false https://doi.org/10.1007/978-3-540-89332-5