Malaria remains a major public health concern in Ethiopia, particularly in the Amhara Region, where seasonal and unpredictable transmission patterns make prevention and control challenging. Accurately forecasting malaria outbreaks is essential for effective resource allocation and timely interventions. This study proposes a hybrid predictive modeling framework that combines time-series forecasting, multi-output regression, and conventional regression-based prediction to forecast the incidence of malaria. Environmental variables, past malaria case data, and demographic information from Amhara Region health centers were used to train and validate the models. The multi-output regression approach enables the simultaneous prediction of multiple outcomes, including Plasmodium species-specific cases, temporal trends, and spatial variations, whereas the hybrid framework captures both seasonal patterns and correlations among predictors. The proposed model exhibits higher prediction accuracy than single-method approaches, exposing hidden patterns and providing valuable information to public health authorities. This study provides a valid and repeatable malaria incidence prediction framework that can support evidence-based decision-making, targeted interventions, and resource optimization in endemic areas.

Weeks after a deadly drone attack on November 30 killed five civilians in the town of Wegel Tena in Ethiopia's Amhara region about 570km (350 miles) north of the capital, Addis Ababa, a witness is still reeling from the trauma. "It's extremely difficult to even describe the scene of the aftermath," said Gebeyehu, who requested use of his first name only for safety reasons. "Bodies were burned so badly they had turned to dust. I saw the finger bones of one of the victims still shaped as though it was still clutching a mobile phone." Several witnesses told Al Jazeera that a drone fired on an ambulance as it approached the Delanta Primary Hospital in Wegel Tena and obliterated it.