A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State

Main Article Content

k. A. Bashiru
O. A. Fasoranbaku
T. A. Ojurongbe
M Lawal
A. A. Abiona
B. A. Oluwasanmi

Abstract

In this study the stochastic process model for estimating the incidence of tuberculosis (TB) infection in Ede kingdom (Ede North and Ede South Local Government Areas) of Osun State was carried out. The probability generating function approach was used to solve the associated birth process model to obtain the estimate of TB incidence. Also time series analysis was carried out using JMulti software to predict future incidence rate of the disease in the study area. Based on Autoregressive Integrated Moving Average (ARIMA) model, the autocorrelation and partial autocorrelation methods and a suitable model to forecast TB infection was obtained.  The goodness of fit was measured using the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Having satisfied all the model assumptions ARIMA (0,1,1) model with standard error, 6.37086 was found to be the best model for the forecast. It was observed that the forecasted series were close to the actual data series.


Keywords: Stochastic process, Tuberculosis, Incidence rate,Ede kingdom

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
Bashiru, k. A., Fasoranbaku, O. A., Ojurongbe, T. A., Lawal, M., Abiona, A. A., & Oluwasanmi, B. A. (2018). A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State. Fountain Journal of Natural and Applied Sciences, 7(1). https://doi.org/10.53704/fujnas.v7i1.182
Section
Articles

References

Aparicio, P. J.,&Carlos Castillo-Chavez (2009). Mathematical Modeling of Tuberculosis Epidemics:. Mathematical Biosciences and Engineering, 6(2), 209 –237.

Bashiru ,K. A. (2014). Stochastic and Dynamic Models Assessment of HIV/AIDS Epidemic in Nigeria. Unpublished PhD thesis. Federal University of Technology, Akure.

Bashiru, K. A., & Fasoranbaku ,O. A. (2009). Statistical Modeling of Mother-to-child and Heterosexual Modes of Transmission of HIV/AIDS Epidemic. The Pacific Journal of Science and Technology, 10(2): 966 –979.

Castilo-Chavez, C., & Feng, Z. (2009). Mathematical Models for the Disease Dynamics of Tuberculosis. New York: Biometrics Unit, Cornell University, Ithaca.

Enagi , A. I., & Ibrahim , M. O. (2012). Preventing Mother to Child Transmission of Tuberculosis Using Bacillus Calmette -Guerin vaccine: A Deterministic Modeling Approach. Research Journal of Mathematics and Statistics, 3(2), 67-71.

Erah, P. O., &Ojieabu, W. A. (2009). Success of the Control of Tuberculosis in Nigeria. International Journal of Health Research,2(1),3-14.

Federal Ministry of Health. (2010). National HIV sero-prevalance Sentinel survey among pregnant women attending antenatal clinics in Nigeria. Abuja, Nigeria: Federal Ministry of Health.

Nainggolan, J., Sudradjat, S., Supriatna, A. K., &Anggriani, N. (2013). Mathematical Model of Tuberculosis Transmission with Recurrent Infection and Vaccination. Journal of Physics Conference Series. 423, 1-2.

Ntthiiri, J. K., Lawi,G.O., Akinyi,C.O., Oganga,D.O., Muriuki,W.C., Musyoka,M.J., Otieno,P.O., & Koechi, L.(2016).Mathematical modeling of typhoid fever disease incorporating protection against infection. British journal of mathematics and computer Science, 14(1), 1-10.

Olagunju, S. O., Bashiru, K. A., &Olowofeso, O. E.(2007). Statistical Model for Estimating the Rate of Spread of Human Immune-Deficiency: A Case Study of Ondo Kingdom. Research Journal of Applied Sciences, 2(10), 1025-1030.

Olowofeso, O. E., &Waema, R. (2005). Mathematical modeling for human immunodeficiency virus (HIV)transmission using generating function approach.Kragujevac Journal of Science, 27, 115 –130.

Tachfouti, N., Slama, K., Berraho, M., &Nejjari, C. (2012). The Impact of Knowledge and Attitude on Adherence to Tuberculosis Treatment. The Pan African Medical Journal. 2012;12, 52doi:10.11604/pamj.2012.1