Spatiotemporal dynamics, hotspot identification and surveillance performance of rubella in Nigeria
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20262688Keywords:
Rubella, Spatial analysis, ARIMA, IgM positivity, Seasonality, GISAbstract
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.
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