MODELING SPATIAL AND TEMPORAL PATTERNS OF MEASLES IN NIGERIA

Measles continues to be a leading cause of morbidity and mortality among children. Numerous factors, including regional and temporal trends that impact the disease's intensity and dissemination, have an impact on the dynamics of measles transmission. This is because there is not a strong modeling framework that can faithfully represent the temporal and spatial dynamics of the disease. Thus, the issue at hand is the lack of a reliable modeling framework capable of precisely capturing the temporal and geographical dynamics of measles. This study aims to model spatial and temporal trends on measles in children under five years. And to identify interaction and autocorrelation effect when measles incidence data were generated from binomial, poison and negative binomial distributions and to fit and forecast measles data using spatial-temporal models at different times and locations in Taraba state. Data on measles susceptibility generated across periods t (where t = 1, 2, 3…) were considered. The model were also examined on monthly data from nine local government areas of Taraba state, Nigeria from the year 2020 to 2023, and the estimated values of the measles susceptibility at a particular region and time are presented. It was concluded that the estimated measles susceptibility increases over time while the interaction and autocorrelation decrease over time for a large number of areal units. The study reveals that the model performs best in modeling data from a binomial distribution across the region and over time. Finally, the predicted measles susceptibility diminishes over time in Taraba state. The fact that the interaction and autocorrelation between the areal unit and time decrease over time, implies that the data is stationary in the long run. Recommend to develop a new model and compare it with the existing ones in checking the interaction and autocorrelation effects.