By Serena Agbor Ashu, DSWB  Master’s Graduate, Cameroon In a world where data is king, we need to close the gap between information and impact, especially when it comes to global health. This hit home when I started my master’s program with the Data Science Without Borders (DSWB) initiative, which is strengthening Africa’s data systems and turning evidence into action. At its core, DSWB is ensuring that African health data is not only collected but also used to address the continent’s major health challenges. Aspiring to be a data scientist, I wanted to hone my skills and expertise and use them for a good cause. What I found was a compelling challenge right in my own backyard: the fight against tuberculosis (TB) in Cameroon.  Despite the high toll of tuberculosis, very little research has used locally generated data to examine treatment outcomes. This gap inspired my thesis, ‘Predictive Modelling of Tuberculosis Treatment Outcomes using Machine Learning Techniques: A Case Study at Douala General Hospital (DGH)’. The hospital [DGH] was grappling with concerns about patient responses to treatment, but most records sat unused in files and fragmented systems. My project provided an opportunity to close that gap by transforming medical records into actionable information that could inform decisions and, ultimately, improve patient outcomes. Hospital records often sit in files or fragmented systems, serving as storage rather than tools for change. Through predictive modeling, I showed how these records could become actionable data, supporting doctors, guiding hospital managers, and equipping policymakers with evidence for better health strategies.  The journey changed me in more than one way. In addition to the technical skills I learned, like advanced analytical methods, machine learning techniques, and working with health data, DSWB gave me access to a group of mentors and peers who believe that data can be used to solve real-world problems. In this space, data was not just numbers or models; it was a lifeline, a means of connecting science to society and turning evidence into impact. That shift in perspective changes how I think about research. It is not just about contributing to academia anymore; it is also about asking questions that matter to people and looking for answers that will really make a difference. While my project focused on TB, the potential goes far beyond one disease. Predictive modeling could be applied in hospitals across Cameroon and beyond to tackle maternal and child health, non-communicable diseases, and other pressing challenges. It points to a future where hospitals are not only places of care but hubs of data-driven innovation, where every record strengthens systems and makes them more responsive.  For me, this thesis was one step in a much larger movement that reflects DSWB’s mission to build strong African data ecosystems where local evidence drives local solutions. It showed me that with the right tools, training, and opportunities, young African scientists can transform data into solutions for their communities. Data science, I realized, is more than an abstract discipline, it is a bridge between people, policies, and possibilities. And this is only the beginning.  

By Alvin Nahabwe from Makerere University Centre for Artificial Intelligence with contributions from Christine Ger Ochola, APHRC Africa’s healthcare landscape is one of striking contrasts. According to the Africa-Europe Foundation (2006), the continent carries 24% of the global disease burden while accounting for only 3% of the world’s health workforce and less than 1% of global health expenditure. Further, Africa faces a vast “double burden” of disease, battling persistent communicable illnesses like malaria and HIV while simultaneously confronting a rapidly growing wave of non-communicable diseases (NCDs) such as diabetes and hypertension.  NCDs are projected to become the leading cause of death by 2030, yet primary care systems, historically geared towards infectious diseases, remain poorly prepared. Maternal and child health also remain in crisis, with Africa accounting for the majority of global maternal deaths. According to the United Nations Population Fund (UNFPA), a woman’s lifetime risk of dying from pregnancy-related complications stands at 1 in 39, compared to 1 in 4,700 in high-income countries. These statistics reveal deeper systematic flaws such as chronic underfunding, severe health worker shortages, and inadequate infrastructure, with many clinics having no reliable power or internet. This imbalance has created a crisis of access and quality, but it has also sparked a wave of innovation. Across the continent, a new generation of entrepreneurs is harnessing technology to address deep-rooted health challenges. From drones delivering life-saving blood supplies to artificial intelligence (AI)-powered diagnostics in remote clinics, a vibrant healthtech sector is emerging. The central question is whether this digital revolution is truly meeting Africa’s most urgent health needs, and what it will take to bring these innovations into the mainstream. It is in this environment of urgent need that healthtech is finding its place through innovators who are not only building apps but are also rethinking how care is delivered. Telemedicine, for instance, is bridging distances and easing specialist shortages, with companies like Kenya’s Zuri Health and Cameroon’s Waspito using both sophisticated apps and simple messaging tools to connect patients and doctors. In Rwanda, the government’s partnership with the digital health company Babyl has led to the integration of virtual consultations into the national health insurance scheme. This initiative serves as an example of successful public-private collaboration, demonstrating how technology can be used to provide healthcare at scale.  Supply chain innovation is also proving transformative. Africa imports up to 90% of its pharmaceuticals, leaving systems vulnerable to stockouts and counterfeit drugs. Companies like Zipline are using drone deliveries in Rwanda and Ghana to get blood and vaccines to remote clinics quickly, bypassing poor roads. In Nigeria, platforms such as Remedial Health and DrugStoc are directly connecting thousands of pharmacies and hospitals to reliable suppliers, which improves both access and quality. Diagnostics represents a promising frontier in healthtech. That over 500 million Africans lack access to basic blood tests creates a profound barrier to managing chronic diseases. In response, Kenya’s Ilara Health is empowering local clinics by leasing them artificial intelligence (AI)-enabled diagnostic tools. This initiative provides community-level access to essential services like blood analysis and ultrasound, a critical step that not only builds local capacity but also leads to the earlier detection of life-threatening conditions.  Despite these advances, funding often flows toward areas that are commercially viable rather than those of greatest public health need. Telemedicine and logistics attract significant investment because their business models scale easily. By contrast, tackling NCDs or improving maternal health requires long-term, integrated approaches that are harder to monetize. This misalignment means that while innovation thrives, it does not always target the heaviest disease burdens. When these innovations and public health align, the results can be powerful. For example, Zipline’s rapid drone delivery of blood has directly reduced maternal deaths from postpartum hemorrhage. Similarly, Ilara Health’s diagnostic services are addressing the testing gap that is fueling the NCD crisis. Meanwhile, mPharma in Ghana has combined supply chain solutions with financing options for chronic medication, thereby improving both affordability and access.  The path forward requires a more unified ecosystem. Africa’s fragmented regulatory landscape makes it hard for startups to scale across borders, while the digital divide limits the reach of even the best solutions. Initiatives by the Africa Centres for Disease Control and Prevention (Africa CDC) to create a single digital health market are a game-changer. By harmonizing regulations and data systems, they can transform the landscape. The Africa HealthTech Marketplace, for example, is already building trust between governments and innovators, which makes it easier for locally developed solutions to gain credibility and be adopted.  For real change to happen, a new mindset is required; investors must prioritize long-term, system-wide impact over quick profits. Policymakers need to embed technology directly into national health plans and create an enabling environment for innovation. Entrepreneurs must design solutions specifically for Africa’s unique realities, making them affordable, scalable, and resilient in low-resource settings. Technology alone cannot solve Africa’s healthcare crisis, but when aligned with policy, investment, and community needs, it can be a powerful catalyst for building stronger, more equitable health systems.

Written by Steve Bicko Cygu and Christine Ger Ochola Artificial Intelligence (AI) is rapidly transforming industries around the world, and the field of public health is no exception. With the emergence of advanced tools and techniques, AI is redefining how healthcare systems approach disease prevention, diagnosis, treatment, and management. These innovations are improving the efficiency and accuracy of healthcare delivery while offering new ways to address persistent public health challenges. The Data Science Without Borders (DSWB) project is integrating AI into public health efforts across African countries, including Kenya, Cameroon, Ethiopia, and Senegal. By applying AI in real-world settings, the project aims to improve health outcomes through timely data analysis, disease prediction, and strengthened surveillance systems. This blog explores how AI is shaping the future of public health within the DSWB pathfinder institutions. One of the foundational innovations being adopted is federated learning, a machine learning approach that enables the training of AI models without requiring the transfer of sensitive health data to a central server. Instead, institutions develop models locally while sharing insights collaboratively. This approach respects privacy and ensures data security, allowing partners to co-create AI models for predicting diseases and analyzing health outcomes without compromising confidentiality. In addition, the DSWB project addresses the challenge of fragmented health data by using AI-driven tools for integration and harmonization. Many African healthcare systems operate in silos, with data stored in various formats and locations. AI methods are used to clean, align, and standardize datasets across institutions in participating countries, enabling better decision-making through accurate, comprehensive insights. Visualization tools powered by AI further aid interpretation, making it easier for researchers and policymakers to act on the findings. Transparency in AI decision-making is another critical aspect. Public health decisions often have direct impacts on communities, so it’s vital that AI tools offer interpretability. This is achieved through explainable AI (XAI), which allows users to understand how models arrive at predictions. Within the DSWB project, XAI supports trust and accountability, particularly in areas such as disease risk assessment and outbreak forecasting. By offering transparency, these tools empower healthcare professionals to make informed decisions with confidence. The project is also exploring the potential of generative AI, particularly generative pre-trained transformers (GPTs), in creating health reports, simulating scenarios, and developing localized health messages. These models can help automate documentation, generate region-specific communication materials, and support proactive responses to potential health threats. The ability to produce culturally relevant and accessible content can enhance community engagement and improve the impact of health education campaigns. To make AI more accessible to non-technical users, DSWB incorporates no-code platforms that allow users to design and deploy AI models through simple graphical interfaces. These platforms remove the barrier of needing programming knowledge, enabling healthcare professionals and public health officials to engage directly with AI tools. Tools like the I-DAIR no-code platform are already making it easier for partner institutions to build their own AI solutions, promoting wider use of data-driven approaches in health systems. AI also plays a critical role in predictive analytics, using historical and real-time data to identify trends and forecast disease outbreaks. By analyzing patterns from past cases, machine learning algorithms can alert health authorities to potential risks, allowing for earlier intervention and better resource allocation. These predictive models consider a range of variables, including socioeconomic conditions, environmental exposures, and behavioral risk factors, helping to pinpoint high-risk populations. In addition to prediction, AI contributes to the personalization of healthcare. Algorithms can process data from genetic profiles, medical histories, and lifestyle behaviors to tailor treatments to individual patients. This is especially important in managing non-communicable diseases like diabetes, hypertension, and cardiovascular conditions, which are increasingly prevalent across Africa. Personalized treatment guided by AI can improve patient outcomes, reduce adverse effects, and ensure that the right interventions reach the right people at the right time. Another growing application of AI in Africa is in remote health monitoring and telemedicine. With increased access to mobile devices and wearable technologies, AI can analyze health indicators in real time, enabling continuous care even in hard-to-reach areas. Telemedicine platforms enhanced by AI are making it possible for patients to consult with healthcare providers remotely, reducing travel burdens and improving access to timely medical advice. These applications show that AI is not a distant innovation but rather a present answer with the potential for improving public health in Africa. The DSWB project deploys artificial intelligence to solve real-world problems, construct resilient health systems, and enhance inclusive and informed decision-making. The work being done in Kenya, Cameroon, Ethiopia, and Senegal showcases how AI can potentially be used responsibly, ethically, and effectively to improve health outcomes across the continent. As Africa continues to embrace digital transformation, the integration of AI into public health is a major step forward. Initiatives like DSWB, which invest in tools that improve cooperation, protect data privacy, and promote transparency, are paving the way for a more equitable, data-driven healthcare future.

Written by Michael Ochola and Christine Ger Ochola Can you tell us a bit about yourself and your journey leading up to your involvement with the Data Science Without Borders (DSWB) initiative? My name is Michael Ochola, my friends and family like to call me Mike. I grew up in Kisumu, located in the western part of Kenya on the outskirts of the city, with a blend of rural and urban experiences. As a young boy, I enjoyed playing soccer, watching movies, cycling, and was fascinated by planes. We made paper planes and enjoyed seeing them fly. During my primary and high school years, I developed a keen interest in sciences and mathematics, which eventually led me to pursue a course in computing. Fast forward as an undergraduate, data science was at its formative stages, and most of the tools we enjoy today were probably being conceptualized by the founding scholars of data science. I remember taking a course in my fourth year in Artificial Intelligence (AI), though what I learned then was centered around programming logic and the application of heuristics in solving complex computational challenges. An AI class today would be very interesting with buzzwords like machine learning, deep learning, and even federated learning. I later started working as a computer analyst. My first assignment was to develop a web application to manage data for longitudinal population dynamics study, under the guidance of great minds on population dynamics research in Africa. This is where my fascination with data science truly took shape. I remember an occasion when we were doing some data exploration, and my supervisor then could tell that ‘something was a mess’ with the data. I thought he possessed some ‘magical powers’ to be able to tell such a difference between ‘good’ and ‘bad’ data, just like being able at this dispensation, to tell AI-generated vis-a-vee actual content. I wanted to have this ‘power’ too. In hindsight I later learned, it was all about understanding probability distributions; most of the data we collected then was tested against specific probability distributions.  My passion for using data to solve complex challenges and drive impactful decisions led me to strategic roles that honed my skills in data standardization, harmonization, integration, machine learning, model development, and deployment. This led to my natural progression to DSWB, where I collaborate with like-minded professionals across Africa, fostering innovation and knowledge-sharing to address some of the continent’s most pressing health and population challenges. How has being part of the Data Science Without Borders (DSWB) initiative contributed to your growth? Being part of DSWB has significantly broadened my professional horizons. It has provided opportunities to work on high-impact projects, collaborate with a diverse network of experts, and access cutting-edge tools and technologies. The initiative has also enhanced my leadership skills, particularly in managing cross-functional teams and delivering training workshops that empower others to harness the power of data science. How has data science transformed your work? Data science has fundamentally reshaped how I approach problem-solving and decision-making. It has provided me with the skills to develop tools and methodologies to extract meaningful insights from different data formats, enabling evidence-based strategies. With a vibrant data community, open-source tools, and cloud technology, we have used data science to streamline our research processes, enhance data quality, and ensure real-time collaboration among global partners. This is not only applicable to work but can transcend to personal life, for instance, prudent financial management by developing or customizing available tools to visualize one’s monthly or yearly spending by feeding mobile money transactions or monthly bank statements to such algorithms. Therefore, transformation is limitless, bounded only by the scope of one’s imagination and willingness to try out new approaches. Can you share a specific example of a challenge you faced and how data science helped you overcome it? One notable challenge was managing disparate longitudinal mental health datasets collected across multiple sites from different mental health studies in Africa. These datasets had varying formats and standards, making it difficult to derive actionable insights to inform mental health policy recommendations. By leveraging data science techniques, we developed a central data warehouse and implemented an Extract Transform and Load (ETL) pipeline to standardize and integrate the datasets into a unified schema and format. This solution not only improved data accessibility but also enabled seamless analysis and visualization, significantly enhancing the research outcomes. We believe in innovation; the data warehouse schema is dynamically designed to ingest not only mental health but also demographic surveillance system data. DSWB allows us to re-use the technology with some of the pathfinders’ longitudinal data. What do you consider your biggest accomplishment in applying data science, and how has it impacted others around you? Data science has empowered me to develop solutions that transcend the limitations of conventional programming. Consider tasks like categorizing emails as spam or determining the likelihood of a loan applicant defaulting challenges where traditional software approaches often fall short. Leveraging data science techniques, I successfully implemented a predictive model for a digital lending application, which not only reduced loan processing times from hours to mere seconds but also significantly lowered the default risk from nearly 40% to just 10%. At the African Population and Health Research Center (APHRC), we leverage data science to transform the discovery and accessibility of African research data, striving to ensure that future AI products are developed free from bias occasioned by lack of data or metadata discoverability. What inspires you to keep pushing boundaries in data science, and what message would you share with others who are just starting out? What inspires me is the transformative potential of data science to address critical issues and improve lives across Africa. The ability to turn raw data into actionable insights that drive policy and innovation keeps me motivated. To those starting out, I would say: embrace curiosity, seek out strategic opportunities to learn, and don’t shy away from challenges. Data science is a journey of continuous discovery, and the impact you can