Formulation of a spatiotemporal model for analysis of neonatal mortality amidst SDGs intervention. The case of Uganda

Abstract

The study aimed to formulate a dynamic linear model within a Bayesian framework to conduct a spatiotemporal analysis of neonatal mortality in Uganda during the SDGs intervention. The study ably formulated the model based on appropriate health-related covariates while considering the spatial and temporal dimensions of the data whose variable of interest (dependent variable) was a quantitative variable measuring the monthly rates of neonatal mortality (number of newborns dying within their first 28 days of life) at the district level of the country. Through Markov Chain Monte Carlo (MCMC) simulations, the study was able to assess the applicability of the model based on simulated data covering 14 years starting from January 2010. Using a Bayesian approach through the Kalman filtering technique, the study estimated the parameters of the formulated model. The study used the same technique through Gibbs sampling to extract meaningful information from the simulated data and provide reliable forecasts for the rates of neonatal mortality.