Abstract
Malaria remains a leading cause of morbidity and mortality in the developing world, particularly in sub-Saharan Africa. Spatial variability significantly influences efforts to control malaria and its incidence, which remains a serious public health concern in Ethiopia. Using geostatistical methods, this study investigates the environmental factors and spatial distribution of malaria incidence in the Hadiya Zone across woredas in 2022 and 2023. Descriptive analyses revealed consistent spatial heterogeneity, with high incidence rates in Shashogo, Soro, and Misrak Badawacho. Global spatial autocorrelation measures Moran's I (0.558 in 2022 and 0.483 in 2023; p < 0.01) and Geary's C (0.63 and 0.69, respectively) confirmed statistically significant clustering of malaria cases. Local Moran's I analysis identified hot spots in Shashogo, Soro, and Misrak Badawacho, and cold spots in Misha, Duna, and Gombora, indicating localized spatial dependence. Spatial regression analysis, comparing Ordinary Least Squares (OLS) and Spatial Autoregressive (SAR) models, highlighted average maximum temperature (β = 0.945, p = 0.017) and proportion of highland terrain (β = 0.543, p = 0.040) as key predictors of malaria incidence. The SAR model showed superior fit, evidenced by lower AIC and higher log-likelihood values, confirming the influence of spatial dependence. These findings support geographically targeted malaria interventions in high-risk woredas. Limitations include the short study period (2022-2023) and the absence of socioeconomic variables due to lack of household survey and secondary data.