What machine-learning models can be used to classify disease traits? What metrics can be used to assess model accuracy? And is the malaria dataset available through OpenMRS?
A range of machine-learning approaches can be used, including classical models such as logistic regression, support vector machines and random forests, as well as more advanced deep-learning models. The choice depends largely on the type and complexity of the available data. Importantly, disease-trait modelling should go beyond clinical characteristics and, where possible, integrate genetic, imaging, environmental and temporal data to provide a more holistic understanding of disease patterns. Model performance should then be assessed using appropriate validation and accuracy measures suited to the particular problem and dataset. Environmental and geographical differences are also important because disease patterns may vary across populations and regions. The availability of the specific malaria dataset in OpenMRS would need to be confirmed separately.
How can artificial intelligence be integrated into telemedicine to improve access to quality diagnosis and treatment, particularly for people living in remote and underserved areas?
AI can strengthen telemedicine by extending specialist expertise to health facilities and patients in remote areas. In our cervical cancer work at Makerere AI Health Lab, for example, we have developed both web-based and offline applications. The web platform enables specialists, including pathologists, to remotely review and validate AI-generated diagnostic results, while the offline smartphone-based tool supports on-site diagnosis where internet connectivity is limited. This is particularly useful in facilities without specialists, as health workers can obtain expert interpretation without requiring the specialist to be physically present. Beyond diagnostics, AI-enabled telehealth platforms can support teleconsultations, patient monitoring, pathology reporting, virtual assistants and treatment support, ultimately bringing quality healthcare services closer to patients regardless of their location.
How was Somalia One Health stakeholder mapping conducted?
The Somalia One Health stakeholder mapping was conducted using a net-mapping approach to identify key actors involved in One Health and understand their roles, levels of influence, relationships and patterns of collaboration. Stakeholders were drawn from the human, animal and environmental health sectors, including government institutions, development partners, academia and other relevant organizations. Participants helped identify who is involved in One Health activities, how different actors interact, and where influence and decision-making are concentrated. The exercise also highlighted coordination gaps and opportunities for stronger collaboration, providing evidence to inform stakeholder engagement, strengthen government leadership and improve the implementation and institutionalization of One Health in Somalia.
What key actions should Somalia prioritize to strengthen the implementation and sustainability of the One Health approach?
Somalia should prioritize stronger integration of environmental health interventions, particularly WASH programs and drought risk management, to reduce disease risks and build community resilience. Sustainable financing mechanisms and stronger national policy frameworks are also needed to support long-term implementation. Coordination among the human, animal and environmental health sectors should be improved through a functional and well-structured One Health platform. In addition, investment in laboratory infrastructure and diagnostic capacity is essential for timely detection of zoonotic and emerging diseases. Continuous training and capacity building for health workers, veterinarians and field personnel will also strengthen service delivery and improve preparedness and response to disease outbreaks.
This is a key finding from the net mapping exercise. While UN agencies and donors currently drive many OH activities, long-term sustainability requires stronger government ownership through improved coordination, domestic financing, policy integration and institutional leadership. The net mapping report provides evidence on key stakeholders, influence pathways and coordination gaps, helping to guide advocacy, strengthen government leadership and support the institutionalization of OH in Somalia.
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