Abstract
BACKGROUND: Despite the availability of an effective vaccine, measles remains a major public health concern in Ethiopia, with recurrent outbreaks and substantial spatial heterogeneity. Understanding its spatio-temporal patterns and determinants is critical for optimizing control strategies and achieving elimination goals. METHODS: A retrospective spatio-temporal analysis was conducted using national measles surveillance data from 2018-2024, aggregated at the zonal level. Geographic clustering was assessed using Moran's I, Getis-Ord Gi(*), and Local Indicators of Spatial Association (LISA) statistics. A negative binomial regression model incorporating spatial and temporal effects was fitted to identify determinants of measles distribution, integrating epidemiological, environmental, nutritional, and socioeconomic variables. RESULTS: Between 2018 and 2024, 71,635 measles cases were reported, with the highest burdens observed in Oromia, Somali, Southern Ethiopia, and parts of Amhara. Significant spatial clustering was detected (Moran's I = 0.154, p = 0.003), with persistent hotspots in southern and southwestern zones. The model showed that higher night-light intensity (IRR = 2.21, p < 0.001) and temporal (IRR = 1.24, p = 0.028) and spatial lag effects (IRR = 1.73, p < 0.001) were strongly associated with increased measles incidence. Higher temperature (IRR = 0.78, p = 0.005) and relative wealth index (IRR = 0.40, p < 0.001) were inversely associated, while underweight prevalence and distance to health facilities were not significant predictors of measles distribution. CONCLUSION: Measles transmission in Ethiopia exhibits clear spatial clustering and temporal persistence, strongly influenced by socioeconomic inequities, human concentration, and climatic conditions. Incorporating spatio-temporal modeling into routine surveillance can enhance early detection and guide geographically targeted immunization, nutrition, and equity-focused interventions toward measles elimination.