Modelling Neonatal Mortality Rate in Nigeria Using a Continuous Poisson-Lindley Distribution
Gerald Ikechukwu Onwuka *
Department of Mathematics and Statistics, Faculty of Physical Sciences, Kebbi State University of Science and Technology, P.M.B. 1144, Aliero, Nigeria .
Abraham Iorkaa Asongo
Department of Statistics & Operations Research, MAUTech, P. M. B. 2076, Yola, Nigeria.
Ishako Ara Bako
Department of Epidemiology and Community Medicine, College of Medicine, Federal University Lafia,Nasarawa State, Nigeria.
Collins Aondona Ortese
Department of Mathematics and Computer Science, Benue State University, Makurdi, Nigeria.
Hassan Allahde
Department of Statistics, Shehu Idris College of Science and Technology, Makarfi, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Nigeria’s effort to reduce under-five mortality has been biased in favour of childhood mortality to the neglect of neonates and as such the literature is short of adequate information on the determinants of neonatal mortality, whereas studies have shown that about half of infant deaths occur in the neonatal period. Knowledge of the determinants of neonatal mortality is essential for the design of intervention programmes that will enhance neonatal survival. Therefore, this study was conducted to investigate the trends in neonatal mortality in Nigeria. It also proposed a Poisson based continuous probability distribution called Poisson-Lindley distribution to neonatal mortality rate in Nigeria. Some properties of the new model and other relevant measures were obtained. The unknown parameters of the model were also estimated using the method of maximum likelihood. The fitness of the proposed model to the neonatal mortality rate was considered using a dataset on neonatal mortality rate from 1967 to 2019.
Keywords: Neonatal mortality rate, poisson distribution, lindley distribution, poisson-lindley distribution, statistical properties, parameter estimation, applications.