I am a Master of Science in Biometry student set to graduate in September 2020. My thesis was on Spatial Survival Analysis of the Risk Factors of under-five Mortality in Kenya; my background is in statistics, data science, machine learning and analytics. I am currently working for The Consulting House as a statistician; and with a keen interest in the development of the society and community. I possess excellent data analysis skills and using relevant data I can come up with various recommendations for the problems facing our societies, including but not limited to the developing countries. I have had experiences doing street clean ups, visiting children homes, contributing towards cancer patients' welfare and teaching. My abilities include leadership, working under minimal supervision, enthusiasm and the willingness to learn and work under new challenging environments. I also possess excellent communication, report writing and presentation skills. I’m passionate about analytics, machine learning and big data.
Project Title: A spatial survival model for risk factors of under-five child mortality in Kenya
Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from region to region due to diversity in socio-economic and even climatic factors. Recently, the country was split into 47 different counties and health care devolved to those county governments further aggravating the spatial differences in health care from county to county. This study examines the spatial variation in the risk factors of Under 5 Child Mortality (U5CM) in Kenya. Data from the Kenya Demographic Health Survey (KDHS-2014) with newly introduced counties was used to analyze this risk. Using a spatial cox proportional hazard model, an intrinsic conditional Autoregressive Model was fitted to account for the spatial variation among the counties in the country while the Cox model was used to model the risk factors associated with time to death of a child. Inference on the risk factors and the spatial variation was made in a Bayesian setup based on the MCMC technique to provide posterior estimates. Our findings showed that there exists a significant spatial variation on the risk of mortality in the country. Different rates and determinants for under-five mortality were observed from county to county. Counties in central Kenya have the highest hazard of death, while western counties have the lowest hazard of death. Demographic factors such as sex of the child and the household head, and social economic factors such as level of education account for most variation when the spatial differences are accounted for. The findings can help the country to plan on health care intervention at a subnational level, by ensuring that counties with higher risk of under five mortality are considered differently from counties experiencing much less risk of death.