Time Series Techniques for Modeling Diphtheria Outbreaks in Kano, Nigeria

Abstract

Despite a globally implemented vaccination program, diphtheria is still one of the major public health threats in areas with low vaccination coverage and slow response to outbreaks. Currently, Kano State, Nigeria, is one of the regions most affected by the recent resurgence of diphtheria. This research aims to forecast the number of diphtheria cases on a monthly basis in the upcoming few months in Kano, utilizing surveillance data from the Nigeria Centre for Disease Control (NCDC), conducted between November 2024 and March 2025. The fitted model was used to generate a seven-month forecast, extending the series to a full 12-month horizon. The forecast captured the observed upward trend while providing 95% prediction intervals, offering valuable insight for public health preparedness and policy decisions.
Overall, the use of time series analysis has proven effective in modeling diphtheria trends in Kano State. This work sets a foundation for real- time epidemic surveillance and predictive modeling in Nigeria, especially in resource-constrained settings where early detection and response are critical.