# Formulation of a spatiotemporal model for the analysis of neonatal mortality amidst SDG interventions: The case of Uganda

## TL;DR

This study creates a spatiotemporal model to analyze neonatal mortality in Uganda during SDG interventions.

## Contribution

The paper introduces a Bayesian dynamic linear model for spatiotemporal analysis of neonatal mortality.

## Key findings

- The model was applied to simulated data over 14 years to assess neonatal mortality trends.
- Bayesian techniques like Kalman filtering and Gibbs sampling provided reliable forecasts.
- The model helps evaluate the impact of SDG interventions on neonatal mortality rates.

## Abstract

This study aimed to formulate a dynamic linear model within a Bayesian framework to conduct a spatiotemporal analysis of neonatal mortality in Uganda during SDG interventions. This study formulated a 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. Through Markov chain Monte Carlo (MCMC) simulations, the applicability of the model could be assessed using simulated data covering 14 years, starting in January 2010, to evaluate the situation before and after the implementation of interventions to achieve the SDGs targets. Using a Bayesian approach through the Kalman filtering technique, the parameters of the formulated model were estimated. This 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.