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Tom Mboya University

PROF. JACOB OTIENO ONG’ALA

PROF. JACOB OTIENO ONG’ALA

PROF. JACOB OTIENO ONG’ALA

Faculty of Physical and Biological Sciences

Prof. Jacob Otieno Ong’ala holds a PhD in Applied Statistics, an MSc in Applied Statistics, and a BSc in Applied Statistics — all from Maseno University, Kenya. He is currently an Associate Professor of Statistics in the Faculty of Biological and Physical Sciences at Tom Mboya University. Prof. Ong’ala has over fifteen years of university teaching, research, and consultancy experience across Africa.
Before joining Tom Mboya University, he served as Head of the Department of Mathematics and Statistics at the Open University of Kenya, Senior Lecturer at the Namibia University of Science and Technology, and Lecturer at Masinde Muliro University of Science and Technology.
His academic and research work focuses on the development of statistical methodologies for the analysis of complex spatio-temporal, epidemiological, and environmental data. He has published widely in international refereed journals and has successfully supervised graduate students at MSc and PhD levels.
Prof. Ong’ala has also participated in several externally funded projects, including collaborations with the Kenya Agricultural and Livestock Research Organization (KALRO) and has served as an external examiner for University of Namibia.
Research Interests
• Multilevel and hierarchical modeling
• Spatio-temporal data analysis
• Epidemiological and environmental statistics
• Machine learning and data-driven policy analytics
• Stochastic and time series modeling

jongala@tmu.ac.ke

Publications

1. Oryiema R., Angwenyi D., Nyogesa A., & Ong’ala, J.O. (2022). Initialization and Estimation of Weights and Bias using Bayesian Technique. Asian Journal of Probability and Statistics, 17(2): 61–85. https://doi.org/10.9734/ajpas/2022/v17i230420
2. Hendricks, H., Ong’ala, J.O., & Ntirampeba, D. (2022). Modification of the Ornstein Uhlenbeck Process to Incorporate the Influence of Speculation on Volatility in Financial Markets. Journal of Mathematics and Statistics, 18(1): 1–10. https://doi.org/10.3844/jmssp.2022.1.10
3. Ndubano, M., Ntirampeba, D., & Ong’ala, J.O. (2021). Multidimensional Poverty Modelling for Namibia Using the Beta Distribution. International Journal of Statistics and Probability, 10(6): 47–58. https://doi.org/10.5539/ijsp.v10n6p47
4. Bushira, K.M., & Ong’ala, J.O. (2021). Modeling Transmission Dynamics and Risk Assessment for COVID-19 in Namibia Using Geospatial Technologies. Transactions of the Indian National Academy of Engineering. https://doi.org/10.1007/s41403-021-00209-y
5. Ong’ala, J.O. (2020). Modelling COVID-19 Transmission Dynamics: Possible Scenarios in Namibia. Asian Journal of Probability and Statistics, 14(7): 371–387.
6. Kisabuli, J.N., Ong’ala, J.O., & Odero, E. (2020). Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care. Asian Journal of Probability and Statistics, 8(4): 38–47.
7. Musyoki, M.N., Ong’ala, J.O., & Wawire, N. (2018). Modeling Agricultural GDP of Kenyan Economy Using Time Series. Asian Journal of Probability and Statistics, 2(1): 1–12.
8. Mwanga, D., Ong’ala, J.O., & Orwa, G. (2017). Modeling Sugarcane Yields in the Kenya Sugar Industry: A SARIMA Forecasting Approach. International Journal of Statistics and Applications, 7(6): 280–288.
9. Makini, F.W., Kamau, G.M., Mose, L.O., Ong’ala, J., Salasya, B., Mulinge, W.W., & Makelo, M. (2017). Status, Challenges, and Prospects of Agricultural Mechanisation in Kenya: The Case of Rice and Banana Value Chains. FARA, 1(2): 24.
10. Ong’ala, J.O., Mwanga, D., & Nuani, F. (2016). On the Use of Principal Component Analysis in Sugarcane Clone Selection. Journal of the Indian Society of Agricultural Statistics, 70(1): 33–39.
11. Ong’ala, J.O., Mulianga, B., Wawire, N., Riungu, G., & Mwanga, D. (2015). Determinants of Sugarcane Smut Prevalence in the Kenya Sugar Industry. International Journal of Agriculture Innovations and Research, 4(1): 2319–1473.
12. Ong’ala, J.O., & Mutai, D.M. (2015). Application of Time Series Model for Predicting Future Adoption of Sugarcane Variety: KEN 83-737. Scholars Journal of Physics, Mathematics and Statistics, 2(2B): 196–204.
13. Ong’ala, J.O., Mugisha, J., & Oleche, P. (2014). A Probabilistic Estimation of the Basic Reproduction Number: A Case of Control Strategy of Pneumonia. Science Journal of Applied Mathematics and Statistics, 2(2): 53–59.
14. Ong’ala, J.O., Wawire, N., Jamoza, J., Maina, P., Ong’injo, E., & Otieno, V. (2013). An Economic Selection Index that Combines Cane Yield and Sugar Content in Identifying Superior Sugarcane Clones in Kenya. African Crop Science Conference Proceedings, 11: 739–743.
15. Ong’ala, J.O., Mugisha, J., & Oleche, P. (2013). Mathematical Model for Pneumonia Dynamics with Carriers. International Journal of Mathematical Analysis, 7(50): 2457–2473.
16. Olweny, C., Ong’ala, J.O., Dida, M., & Okori, P. (2013). Farmers’ Perception on Sweet Sorghum (Sorghum bicolor [L] Moench) and Potential of its Utilization in Kenya. World Journal of Agricultural Sciences, 1(2): 65–75.
17. Ong’ala, J.O., Stern, D., & Stern, R. (2012). Extending GenStat Capability to Analyze Rainfall Data Using Markov Chain Model. European Scientific Journal, 8(17): 65–75.
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