Abstract

The study examined the incidence of the rate of Neonatal Mortality in Nigeria. The incidence tested with use of Time Series Analysis (ARIMA). The trend plot of the incidence shows that there has been steady decrease in the incidence rate over the years. The series was stationarity using the Augmented Dickey-Fuller (ADF) Test, the result found to be stationary. The Correlogram of the incidence also supports the stationarity of the series at 1% level of significance. The model that best describes the incidence was tested using the Box-Jenkins four step procedures, which involve identification, explanation, diagnostic and forecasting. ARMA (1, 1), and ARIMA (1, 1, 1) models were tested, and ARIMA (1, 1, 1) model happens to be the best model that best fits the series. The time series analysis shows that neonatal mortality rate has reduced by 17.8% from 51. 7% in the year 1990 to 33. 9% in the year 2017.Keywords: Auto regression, Moving average, Mortality, Stationarity, Correlogram

Highlights

  • Neonatal mortality is a significant public health problem worldwide, and accounts for more than 60% of newborn deaths before their first birthday [UNICEF 2008], neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year

  • Method of Data Analysis: The methods of data analysis that will be employed in this study is the Time Series Analysis using Autoregressive Integrated Moving Average (ARIMA)

  • The mean absolute deviation (MAD) = 1.05 which indicates that the series of good fit, as the lower the MAD, the better the fit of the series

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Summary

Introduction

Neonatal mortality is a significant public health problem worldwide, and accounts for more than 60% of newborn deaths before their first birthday [UNICEF 2008], neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year. Method of Data Analysis: The methods of data analysis that will be employed in this study is the Time Series Analysis using Autoregressive Integrated Moving Average (ARIMA).

Results
Conclusion

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