Deep learning model meets community-based surveillance of acute flaccid paralysis.

Acute flaccid paralysis (AFP) case surveillance is pivotal for the early detection of potential poliovirus, particularly in endemic countries such as Ethiopia. The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance. However, challenges like delayed detection and disorganized communication persist.

Waning success: a 2013-2022 spatial and temporal trend analysis of malaria in Ethiopia.

Background: Despite consecutive decades of success in reducing malaria transmission, Ethiopia went off track towards its goal of malaria elimination by 2030, as outlined in the NMCP malaria strategy. Recent malaria outbreaks in Ethiopia are attributed to the emergence and spread of diagnostic and drug-resistant Plasmodium falciparum, increased insecticide resistance in major vectors and the spread of invasive Anopheles stephensi. The effects of the COVID-19 pandemic, environmental anomalies and internal conflicts have also potentially played a role in increasing malaria transmission.

Larval source management in Ethiopia: modelling to assess its effectiveness in curbing malaria surge in dire Dawa and Batu Towns.

Background: Ethiopia faces several severe challenges in terms of malaria elimination, including drug resistance and diagnostic evasion in the Plasmodium falciparum parasite, insecticide resistance in the primary Anopheles malaria vector, and, most recently, the invasion of the Asian malaria vector Anopheles stephensi. Novel malaria control methods are therefore needed, and in this paper, we describe the evaluation of a larval source management (LSM) strategy implemented in response to An. stephensi.

Decolonizing global AI governance: assessment of the state of decolonized AI governance in Sub-Saharan Africa.

Global artificial intelligence (AI) governance must prioritize equity, embrace a decolonial mindset, and provide the Global South countries the authority to spearhead solution creation. Decolonization is crucial for dismantling Western-centric cognitive frameworks and mitigating biases. Integrating a decolonial approach to AI governance involves recognizing persistent colonial repercussions, leading to biases in AI solutions and disparities in AI access based on gender, race, geography, income and societal factors.