Measles is an extremely contagious virus that mainly affects youngsters; however, it can strike anyone at any age. In malnourished people, it can cause serious complications, such as pneumonia, and even death of malnourished individuals. Controlling measles requires information on the future forecast or spread. Nigeria continues to represent a substantial portion of global measles mortality due to persistent gaps in information on the future forecast, surveillance, and others. This study examines measles trends in Nigeria from 2012 to 2022 to identify a suitable time series model for forecasting the measles. An ARMA model was applied, and diagnostic tools like ACF, PACF, AIC, and BIC were used for model selection. The selected ARMA (1,1) model demonstrated a good fit for the data, allowing for accurate predictions of measles. These predictions can inform public health strategies and interventions aimed at improving vaccine coverage and enhancing surveillance in Nigeria. Model adequacy was verified using the Ljung-Box test which shows that the model is adequate and well fitted for the forecast. Analysis reveals a cut off of ACF plot after lag 3, and that of PACF after lag 1. These findings can support policy makers in planning effective disease control strategies.

Index Terms- Forcasting, measles, time series, mortality, estimation