Comparing weighted Markov chain and auto-regressive integrated moving average in the prediction of under-5 mortality annual closing rates in Nigeria

In developing countries, childhood mortality rates are not only affected by socioeconomic, demographic, and health variables, but also vary across regions. Correctly predicting childhood mortality rate trends can provide a clearer understanding for health policy formulation to reduce mortality. This paper describes and compares two prediction methods: Weighted Markov Chain Model (WMC) and Autoregressive Integrated Moving Average (ARIMA) in order to establish which method can better predict the annual child mortality rate in Nigeria. The data for the study were Childhood Mortality Annual Closing Rates (CMACR) data for Nigeria from 1964-2017. The CMACR provides random values changing over time (annually), so we can analyze the mortality closing rate and predict the change range in the next state. Weighted Markov Chain (WMC), a method based on Markov theory, addresses the state and its transition procedures to describe a changing random time series. While the Autoregressive Integrated Moving Average (ARIMA) is a generalization of an Autoregressive Moving Average (ARMA) model. The findings indicate that the ARIMA model predicts CMACR for Nigeria better than WMC. The WMC entered in a loop after two iterations, and we could not use it effectively to predict the future values of CMACR.