MODELLING THE INCIDENCE OF POST-NEONATAL MORTALITY RATE IN SPECIALIST HOSPITAL BAUCHI, USING AUTO REGRESSIVE INTEGRATED MOVING AVERAGE
Abstract
Post-neonatal mortality remains a significant public health concern, partic-
ularly in developing regions where access to quality healthcare is limited. This
study focuses on analysing and forecasting post-neonatal mortality trends at
the Specialist Hospital in Bauchi, Nigeria, using time series modelling tech-
niques. The research aims to identify patterns in post-neonatal mortality rates
and provide predictive insights that can guide healthcare interventions. Method-
ologically, the study applies time series analysis using the Auto-Regressive In-
tegrated Moving Average (ARIMA) model. The data used consists of recorded
post-neonatal mortality cases from the Specialist Hospital Bauchi, covering the
period from 2014 to 2023.This research highlights the need for improved health-
care policies, enhanced child health programs, and strategic interventions aimed
at reducing post-neonatal mortality in Bauchi. By leveraging time series fore-
casting, stakeholders can make data-driven decisions to strengthen healthcare
systems and improve child survival rates.
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