Publication:

Spatiotemporal dynamics, hotspot identification and surveillance performance of rubella in Nigeria

Background Rubella remains endemic in Nigeria due to the absence of nationwide integration of rubella-containing vaccine (RCV) into routine immunization. Understanding spatial clustering, seasonal trends, and surveillance performance is critical for informing vaccination policy and outbreak preparedness.

Methods A retrospective observational study was conducted using national rubella surveillance data (2020–2024). Laboratory-confirmed IgM-positive cases were analyzed using geospatial and time-series methods. Spatial clustering was assessed with Moran’s I and Getis-Ord Gi statistics. Seasonal patterns were examined using seasonal-trend decomposition via loess (STL), and forecasting was performed with autoregressive integrated moving average (ARIMA) models. Surveillance performance was assessed using WHO timeliness indicators.

Results Among 7,907 suspected cases, 4,001 (50.6%) were IgM-positive. The South East zone recorded the greatest burden (21.2%), with Ebonyi State identified as a significant hotspot (Gi=2.32, p=0.020). Moran’s I (0.143; p=0.054) indicated weak spatial autocorrelation. Rubella incidence peaked consistently in May, with 2022 recording the highest annual total (1,065 cases; 26.6%). ARIMA forecasting projected 176 cases for May 2025 (95% CI: 72–279). Although 92.4% of laboratory results were released within seven days, only 6.5% met complete surveillance timeliness criteria due to specimen transport delays.

Conclusions These findings provide empirical evidence to support the introduction of rubella-containing vaccines, targeted catch-up immunization, and strengthening of surveillance logistics to improve outbreak preparedness.