Laboratory capacity building during COVID-19 in Somalia: improving access to essential diagnostics for national health security in a fragile setting.
Published in Biomedical Research
The COVID-19 pandemic is remembered as a disease that has shaken the tenets of global health in a significant way. For over three years, from 2020 to 2022, it dominated as the leading cause of cases, hospitalizations, and deaths due to infectious disease, bagging the infamous title of the blockbuster virus of the decade. However, amidst the devastation, the pandemic exposed the underbelly of health security, exposing the gaps in preparedness, response, and recovery.
Additionally, due to limited countermeasures available in the early phase of the disease, testing and tracking infected individuals become the only available weapon to curtail transmission. This situation placed laboratories, a traditionally neglected sector of the healthcare system, at the center of the response. Indeed, it was time for the laboratory system to emerge from its neglect and assert its essential role in the health security landscape. While countries capitalized on this opportunity to develop their laboratory sectors, it was a particularly valuable opportunity for nations facing resource limitations and ongoing conflicts.
For the Federal Republic of Somalia, the scale-up of the infrastructure and health workforce capacities, along with the provision of state-of-the-art equipment and supplies achieved during this period, was unprecedented compared to any previous intervention from the government, humanitarian/development partners, or the private sector. These efforts were essential to expand access to testing for the population living in diverse settings, some of which were inaccessible due to insecurity. It was deemed important to share this experience through a scientific publication as a successful health program case study and to set a precedent for countries in similar contexts. Therefore, our articles illustrate how the opportunity was effectively utilized to transform the country’s laboratory system showing key milestones in the laboratory sector.
While similar narratives have emerged from other nations, such as Yemen, the context in Somalia differed fundamentally in several respects, some of which are described below:
Starting from scratch
Since the country is emerging from three decades of internal civil conflict, the central government continues to struggle to fulfill its statutory obligations, including providing essential healthcare. As a result, healthcare infrastructure is severely damaged due to years of neglect, the demand for services is overstretched, and the health workforce has limited capacity. Functional laboratories in the country were few and far between, making establishing capacities, especially at the State level, quite challenging owing to the lack of basic amenities like water and electricity.
The nightmare of non-state actors
The laboratory improvement program faced particularly significant challenges because it targeted a third of the population living in the precincts of de facto Non-State actors. Implementing health programs and testing services to hard-to-reach populations sometimes exposed us to risks. Ultimately, achieving the goal outweighed the fear, and the results justified the calculated risks.
Co-occurring health emergencies
The COVID-19 pandemic coincided with persistent outbreaks in the country, such as cholera and measles, which often diverted resources and focus, impeding implementation. Despite this, the global attention on COVID-19 at that time ensured that interventions to address the pandemic remained a top priority.
Of the many achievements reported in the paper related to laboratory and surveillance improvements, two main areas deserve a mention. First, laboratory capacity for molecular, bacteriological, and serological testing has been significantly expanded and improved to enhance surveillance of outbreak-prone diseases in the country. This development strategically positions the country’s public health laboratories on the road to recovery from many years of neglect. It also improves the country’s IHR core capacity and health security index score. Figure 1 below shows the progressive improvement in the number of tests for different endemic diseases.
Figure 1: Showing the number of tests for different endemic diseases performed in public health laboratories between 2020 and 2023.
Second, establishing three genomic sequencing laboratories represents a transformative step toward access to innovative technologies, even in resource-limited settings. For countries reliant on humanitarian agencies for essential health programs, such as immunization programs, this kind of investment would typically be unfeasible during peacetime. However, the urgent necessity for in-country capacity to monitor circulating variants and the limited ability to refer samples outside the nation have underscored the importance of developing substantial local capabilities.
Next steps:
Establishing the infrastructure and installing the equipment are only a few pieces of the puzzle. The major task will be to sustain the progress and ensure the provision of the resources required to maximize the utilization of the established capacity. As interest in COVID-19 waned, complacency has already set in. However, to fill the gaps and ensure the functionality of this investment, leadership and prioritization in resource allocation will be required both at the MOH level and in critical health programs such as WHO.
In conclusion:
The COVID-19 pandemic was a lifetime opportunity for resource-limited, humanitarian-dependent countries to improve their laboratory systems. As reported in our article, this opportunity was well utilized in Somalia. The material infrastructure and human capacity built will be a national asset that will be useful for the generations to come. The lesson learned from COVID-19 should be a wake-up call for countries to prioritize investment in their laboratory systems for the safety and security of their population and as the first line of defense against infectious diseases. In the meantime, laboratory strengthening programs should be viewed as a critical and indispensable part of the healthcare system, even in the context of emergencies and resource limitations. Moreover, resources should be deliberately planned and allocated to ensure continuous improvement while sustaining the gains.
Parting shot—As leaders assemble for the World Health Assembly in Geneva between May 19th and 27th, 2025, to discuss global health issues, the role of laboratories in ensuring health security should be a standing agenda item followed by actionable resolutions. Indeed, the world should not wait for the next pandemic to realize the critical role laboratory programs play in ensuring health security for all.
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Public Health Professionals’ Education
Strengthening the education and continuous professional development of public health professionals is fundamental to achieving resilient, equitable, and responsive health systems. Recent global experiences including pandemics, protracted humanitarian crises, climate-related health risks, rapid urbanization, and technological transformation have highlighted both the critical role of the public health workforce and persistent gaps in training relevance, scale, quality, and sustainability. In many settings, educational models remain insufficiently aligned with real-world system needs, emerging competencies, and evolving career pathways.
While there is growing innovation in public health education, evidence and lessons learned are dispersed across disciplines and regions. A focused supplement in the Journal of Epidemiology and Global Health will provide a timely scholarly platform to consolidate high-quality research and practice-based insights.
The proposed Collection aims to advance knowledge in the education of public health professionals by:
Showcasing empirical evidence and innovative models for public health education and training
Examining how educational approaches align with health system priorities and workforce needs
Informing policy, institutional strategies, and investment in public health workforce development
Promoting equity, quality, and sustainability in public health education globally.
Scope and Thematic Areas
The supplement will invite original research articles, systematic or scoping reviews, and rigorously documented practice-based papers across the following thematic areas:
Competency-Based Public Health Education
- Core and advanced competency frameworks
- Alignment of curricula with population health needs and system priorities
Field-Based and Applied Learning Models
- Field Epidemiology Training Programs (FETPs) and similar applied training models
- Experiential learning, service-based education, and community engagement
Interprofessional and Multisectoral Education
- Collaborative training across health, social, environmental, and humanitarian sectors
- Preparing public health professionals for whole-of-government and whole-of-society approaches
Digital, Blended, and Distance Learning Innovations
- Online and hybrid training models
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Education for Health Emergencies and Fragile Settings
- Workforce training for outbreak preparedness, humanitarian response, and conflict settings
- Adaptive education models in fragile and resource-constrained contexts
Equity, Ethics, and Inclusion in Public Health Training
- Gender, geographic, and socioeconomic equity in access to education
- Ethical dimensions of training, mentorship, and professional advancement
Leadership, Management, and Systems Thinking
- Training for public health leadership, governance, and policy engagement
- Building managerial and strategic competencies for system-level impact
Mentorship, Supervision, and Career Pathways
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Partnerships, Financing, and Institutionalization
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All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.
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Artificial intelligence in health
How AI shapes medicine and human health
Artificial intelligence (AI) in health now spans large language models, computer vision, multimodal foundation models, predictive analytics, and decision-support systems. 1 2 These tools can process clinical text, imaging, physiological signals, laboratory data, patient history, and public health information at scale, and they are being piloted to support tasks ranging from documentation and prescription renewal to diagnostic interpretation, risk stratification, surveillance, and health-system operations.3 As adoption accelerates, evidence on real-world performance, safety, equity, and downstream health effects remains limited.
Large language models are one important branch of AI in health, but they are only part of a broader landscape that also includes computer vision for medical imaging, machine learning for prediction and classification, multimodal models that combine text and images or signals, and AI tools embedded in clinical and public health workflows. Across all of these approaches, performance depends on data quality, representativeness, validation, and the context in which the system is deployed.
LLMs are systems designed to predict the most likely next word. The word “Large” implies the vastness of written data accessible electronically—including books, web data, and articles—used in these predictions. The word “model” implies that LLMs do not know facts as humans do; they operate on probability. When we type or ask a question (called a prompt), the model predicts the next most likely option, called a “token,” based on the patterns it learned during training.1 This happens in a non-linear manner, meaning it processes words in relation to all other words in a sentence rather than one by one, helping it understand context.
In simple terms, the computing framework used in an LLM, the transformer, finds all possible routes on a map (neural networks) to reach a destination and then highlights the best options based on the logic it was trained to follow. The models are only as good as the data available to them and the training and fine-tuning they were subjected to.
For example, in Utah’s Prescription-Renewal Pilot Program3 launched in January 2026, LLMs are trained (the first 250 outputs require physician review for fine-tuning) and approved to perform an agentic function for 192 commonly prescribed chronic disease medicines that require frequent refills (a repetitive activity for physicians). Tethered to a deterministic logic engine, the system uses a multi-agent AI platform to handle the natural language interaction with the patient and data (patient records, patient-provided information on health status during the refill process, drug pharmacology, and common patient challenges).
AI can assist in patient care, public health surveillance, training of health care professionals, data monitoring, and policy development.2 4 5 Collective use of data can shape policy decisions in public health, including through rapid and live analysis of social media chats or disease report patterns to generate new hypotheses or public health responses.
Deployment of LLMs in healthcare is outpacing the evidence base for real-world performance, equity, and downstream population-health effects.
The challenge ahead
As it is new and rapidly evolving, there is a need to surface critical concerns and risks about AI in health.
Mainstreaming AI is a challenge across disciplines, geographies, and development barriers. Most existing literature treats clinical AI and population-health AI as separate conversations. In practice, a tool deployed at the bedside has immediate epidemiologic and policy consequences—who gets referred, who gets screened, which signals reach surveillance systems, and which populations bear the costs of miscalibration. Public health is yet to exploit the vast analytic power of AI in triaging huge amounts of healthcare delivery data and health-related social media discussions, or the utility of AI in healthcare delivery, including supply chains. There is an imminent need to avert a potential AI access or utilization gap between resource-rich and resource-poor countries.
There is a policy vacuum and regulatory gap regarding AI applications and licensing.
The lack of harmonization remains between the speed at which AI applications are evolving and the response from the regulatory framework.
What can researchers do?
The challenge to researchers is to disseminate information on the potential utility and impact on the healthcare delivery system, and how to steer policy proactively to avert potential drawbacks that are common to all new approaches.
This collection is designed to connect those conversations and to produce evidence useful to clinicians, epidemiologists, and policymakers simultaneously, with particular attention to equity, low- and middle-income country (LMIC) applicability, and unintended consequences. We welcome original research, systematic reviews, policy analyses, and commentaries on generative and non-generative AI across the following areas:
- Clinical and diagnostic domains — development, validation, and real-world evaluation of diagnostic, prognostic, and decision-support tools; LLMs in clinical workflows; computer vision in radiology, pathology, dermatology, ophthalmology, endoscopy, and surgery; multimodal models integrating text, imaging, laboratory, and waveform data; clinician–AI interaction and human oversight.
- Physiological signals, wearables, and remote monitoring — AI for ECG, EEG, ICU monitoring, mobile and home-based sensors, early warning systems, and longitudinal patient monitoring.
- Epidemiology and population health — surveillance, outbreak detection, and forecasting; AI-assisted causal inference and analysis of large observational datasets; applications to communicable and non-communicable disease epidemiology.
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- Ethics, equity, governance, and policy — algorithmic bias and fairness audits; informed consent and transparency; regulatory frameworks and reporting standards (CONSORT-AI, TRIPOD+AI, DECIDE-AI); economic and health-system evaluations; workforce implications.
- Methodological and cross-cutting work — frameworks linking individual-level model performance to population-level outcomes; comparative effectiveness of AI-assisted versus standard pathways; studies of unintended consequences.
- Medical and health system research — development of prompts for large data use and highlighting utility to inform policy; innovations in surveillance systems; improving efficiency of scientific investigations, including methodology and survey development.
We believe the Collection will serve as inaugural contributions on this rapidly evolving interface of medicine, public health, biotechnology, and ethics, collectively enhancing our efforts to continuously improve human health and welfare.
References
1. Teo ZL, Thirunavukarasu AJ, Elangovan K, et al. Generative artificial intelligence in medicine. Nature Medicine 2025;31(10):3270-82. doi: 10.1038/s41591-025-03983-2
2. Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine. Nature Medicine 2022;28(1):31-38. doi: 10.1038/s41591-021-01614-0
3. Gerke S, Parikh RB, Cohen IG. Utah’s Prescription-Renewal Pilot Program — Autonomous AI Managing Patient Care. New England Journal of Medicine 2026;394(16):1561-63. doi: doi:10.1056/NEJMp2601148
4. Panteli D, Adib K, Buttigieg S, et al. Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions. The Lancet Public Health 2025;10(5):e428-e32.
5. Rao VM, Hla M, Moor M, et al. Multimodal generative AI for medical image interpretation. Nature 2025;639(8056):888-96.
All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.
Publishing Model: Open Access
Deadline: Feb 26, 2027
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