Rebel forces in Ethiopia's northern Tigray region have said they have temporarily withdrawn from the regional capital, Mekelle, as fighting with the national army moves towards the city. The Tigray People's Liberation Front (TPLF), which has been running the region, said its decision to relocate was made because of concerns about risks to civilians. Two residents in Mekelle told the BBC that pro-government forces now control the city and TPLF forces have abandoned it. One resident also said mobile phone services appear to have resumed in parts of the city. Fighting has erupted in recent weeks between the TPLF and federal government forces alongside allied fighters in parts of Ethiopia's northern regions of Tigray, Afar and Amhara.

Explosions have rung out in Addis Ababa as fighting between Ethiopia's federal forces and a coalition of armed opposition groups widens in the country's north. Three diplomatic sources told the Reuters news agency on Thursday that the blasts were heard overnight in the Ethiopian capital. The blasts came hours after Addis Ababa instituted a ban on drone-flying in the capital and surrounding areas. The alliance unites seven armed groups, including the Tigray People's Liberation Front (TPLF) and groups from the Amhara, Afar, Somali and Benishangul-Gumuz regions. It says it wants to remove Prime Minister Abiy Ahmed's government.

Share Ethiopia's Gobena, Kenya's Olympic champion Jepchirchir win Sydney Marathon on social media Ethiopia's Addisu Gobena and Kenyan world champion Peres Jepchirchir surged to commanding Sydney Marathon wins in the men's and women's races . It was the first victory in a World Marathon Major for 21-year-old Gobena, who came second last year, while Jepchirchir, in Australia on Sunday, added to her previous triumphs in Boston, New York and London. The dominant run shattered the course record by almost one and a half minutes, with last year's winner Hailemaryam Kiros dropping from the pace in the dying stages. "I am very happy," said Gobena, who was a javelin thrower before transitioning to long-distance running, gaining international attention by winning the 2024 Dubai Marathon. "Last year I came second, I learnt from my mistake and I went back home and I worked on my mistake to perform and break the record today." The unstoppable Jepchirchir was in a league of her own, crossing the finish line in front of the Sydney Opera House in 2:18:31, nearly four minutes ahead of fellow Kenyan Irine Cheptai and Ethiopia's Shure Demise Ware.

Drone strikes in Ethiopia's Tigray kill one amid fears of renewed conflict One person has been killed and another injured in drone strikes in Ethiopia's northern Tigray region, a senior Tigrayan official and a humanitarian worker said, in another sign of renewed conflict between regional and federal forces. The Tigrayan official on Saturday said the drone strikes hit two Isuzu trucks near Enticho and Gendebta, two places in Tigray about 20km (12 miles) apart. A local humanitarian worker confirmed the strikes had happened. Both asked not to be named, the Reuters news agency reported. It was not immediately clear what the trucks were carrying.

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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.

Malnutrition among pregnant women is a major public health challenge in Ethiopia, increasing the risk of adverse maternal and neonatal outcomes. Traditional statistical approaches often fail to capture the complex and multidimensional determinants of nutritional status. This study develops a predictive model using ensemble machine learning techniques, leveraging data from the Ethiopian Demographic and Health Survey (2005-2020), comprising 18,108 records with 30 socio-demographic and health attributes. Data preprocessing included handling missing values, normalization, and balancing with SMOTE, followed by feature selection to identify key predictors. Several supervised ensemble algorithms including XGBoost, Random Forest, CatBoost, and AdaBoost were applied to classify nutritional status. Among them, the Random Forest model achieved the best performance, classifying women into four categories (normal, moderate malnutrition, severe malnutrition, and overnutrition) with 97.87% accuracy, 97.88% precision, 97.87% recall, 97.87% F1-score, and 99.86% ROC AUC. These findings demonstrate the effectiveness of ensemble learning in capturing hidden patterns from complex datasets and provide timely insights for early detection of nutritional risks. The results offer practical implications for healthcare providers, policymakers, and researchers, supporting data-driven strategies to improve maternal nutrition and health outcomes in Ethiopia.

Language identification is the task of determining the languages for a given text. In many real-world scenarios, text may contain more than one language, particularly in multilingual communities. Bilingual Language Identification (BLID) is the task of identifying and distinguishing between two languages in a given text. This paper presents BLID for languages spoken in the southern part of Ethiopia, namely Wolaita and Gofa. The presence of words' similarities and differences between the two languages makes the language identification task challenging. To overcome this challenge, we employed various experiments on various approaches. Then, the combination of the Bert-based pre-trained language model and LSTM approach performed better, with an F1-score of 0.72 on the test set. As a result, the work will be effective in tackling unwanted social media issues and providing a foundation for further research in this area.

As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.