Stochastic Modelling Of Human Under-Five Age Mortality With Spatially Structured County Frailty Effects In Kenya (Published)
Survival analysis examines and models the time it takes for events to occur. The prototypical such event is death, from which the name ‘survival analysis’ and much of its terminology derives. This research study develops a predictive model and considers the factors associated with under-five child mortality rates in Kenya in order to provide the solutions and interventions to organizations concerned with demographic data. As the human lifespan increases, more and more people are becoming interested in mortality. The aim of this study is to estimate the robust and reliable estimates of level and trend in under-five mortality in Kenya . Survival analysis techniques and frailty modeling will be used as the statistical tools for analyzing the time-event data. Both the survival parametric and non –parametric models and the frailty models will be fit to help us draw the required conclusions based on under-five child mortality rates. The results of this study will be used to assist in formulating appropriate health programs and policies that will reduce under-five human mortality.
Comparison of Survival Models and Estimation of Their Parameters With Respect To Mortality in a Given Population: A Case Study of Homa-Bay County in Nyanza Province (Review Completed - Accepted)
In this research , we consider three different survival models under the assumption of Gompertz model as the baseline distribution. We compare the fitting results of the Exponential distribution , the Gompertz distribution and the Gompertz – Makeham model in a given population. As the human lifespan decreases, more and more people are becoming interested in mortality rates at higher ages. The aim of this study is to estimate the robust and reliable estimates of level and trend in mortality in Kenya . The purpose of this study is to find out if the population of Kendu Bay area in Nyanza Province fits the Gompertz model and also to compare different survival models parametrically in a population. And also to determine the relationship between death rate and age in the area. Model comparison is made using the maximum likelihood function technique and a well fitted model is suggested for the population data. The expected output is that the preferred model is the one which satisfies the characteristics of the given population.