Academia · Finance · Global Health · Agriculture · Enterprise
Quantium Insights delivers bespoke machine learning, spatial analytics, and AI-powered decision systems for organisations navigating complex, data-rich environments across five high-impact sectors.
At Quantium Insights, our mission is to empower organisations across academia, finance, global health, agriculture, and enterprise with AI-powered predictive intelligence, advanced analytics, and innovative decision systems that drive measurable impact. We combine expertise in machine learning, spatial epidemiology, financial risk modelling, agricultural analytics, implementation science, monitoring and evaluation, and interactive data visualisation to transform complex data into actionable foresight.
"From data to foresight — predictive models drive real-time early-warning systems that transform signals into action, enabling you to see tomorrow's challenges today." — Dr. Amobi Andrew Onovo, PhD, MPH
Trusted by Leading Global Health Organizations
Practical tools built for public health practitioners, clinicians, and researchers across Africa and beyond.
Book a consultation with Dr. Amobi Andrew Onovo and discover how AI-powered analytics can accelerate your mission.
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Expert consulting services leveraging advanced statistical analysis, predictive modeling, spatial epidemiology, and implementation science to empower organizations with scalable AI solutions.
Leverage advanced machine learning techniques, including XGBoost, neural networks, and Bayesian models, to generate accurate forecasts and anticipate critical trends in health and enterprise settings. Build and deploy high-accuracy ML models for outbreak prediction, coverage gaps, clinical risk, and program optimization.
Apply advanced geostatistical techniques, including empirical Bayesian kriging, geographically weighted regression, and spatial clustering, to uncover geographic disparities in disease burden and program impact. Map transmission risks, service gaps, and vulnerable populations to guide targeted outreach.
Design and rigorously evaluate causal interventions using advanced causal inference methods, including difference-in-differences, A/B testing, and instrumental variable analysis, to precisely isolate program impact and optimize delivery strategies. Develop adaptive implementation plans that maintain fidelity while allowing flexibility.
Design end-to-end M&E systems aligned with strategy and donor requirements (PEPFAR MER, WHO, national). Automate key indicators, integrate DHIS2 and real-time dashboards, and embed AI-powered data quality tools for accurate, timely reporting. Conduct needs assessments, mid-term and endline evaluations.
Craft intuitive, interactive dashboards with Tableau, Power BI, Streamlit, and Gradio to translate complex data into clear narratives that inform strategic decision-making. Deploy secure, cloud-hosted analytics platforms accessible to program teams in real time.
Deliver tailored workshops and hands-on training covering data management, AI/ML applications in health, GIS, BigQuery, ArcGIS Pro, and scientific writing. Design E-learning modules and modelling fellowships that build sustainable institutional capacity for data-driven decision making.
"From data to foresight — predictive models drive real-time early-warning systems that transform signals into action, enabling you to see tomorrow's challenges today." — Dr. Amobi Andrew Onovo
Build and deploy time-series forecasting models for outbreak prediction, immunization coverage, HIV cascade performance, and program risk scoring. Deployed for UNICEF, NPHCDA, and global health programs across Sub-Saharan Africa.
An advanced feature engineering and exploratory data analysis (EDA) platform with an integrated Data Science Agent for grounded statistical analysis and interpretation. Enables frontline analysts and researchers to rapidly identify high-impact drivers, assess variable relationships, and generate evidence-based insights from complex multi-dimensional datasets.
XGBoost + SHAP-powered stroke risk prediction with transparent clinical interpretation. Deployed for clinics, wellness programs, and public health research. 400+ user sessions in first week of launch.
Three-layer framework for detecting deceptive alignment and evaluation awareness in LLMs. Published on medRxiv (2026). Available for regulatory agencies, healthcare AI companies, and research institutions.
Dr. Amobi Andrew Onovo is an epidemiologist, data scientist, and AI platform architect with over 15 years of experience in global health analytics across sub-Saharan Africa. He is the Founder and Principal Consultant of Quantium Insights LLC, and serves as an HIV Data Scientist Consultant at UNICEF, supporting rigorous epidemiological data pipelines and integrated surveillance analytics across sub-Saharan African countries.
His work spans the full model lifecycle, from data pipelines and predictive modelling to geostatistical mapping and secure cloud deployment, using supervised and unsupervised learning, deep learning, and advanced AI methods to generate actionable insights for public health programs. He has designed and led analytics that have supported decisions affecting hundreds of thousands of beneficiaries.
His Lancet EClinicalMedicine publication on Bayesian HIV prevalence modelling has been adopted by UNAIDS for global HIV projections. He secured a Swiss National Science Foundation grant exceeding $1 million and has published across HIV, COVID-19, immunization, and pediatric health domains.
Designed automated analytics pipelines integrating DHIS2 and PostgreSQL data across 32,000+ facilities in 21 sub-Saharan African countries. Built multilevel statistical models and hierarchical clustering workflows to profile service gaps across 8,871+ sub-national units, improving identification of underperforming clusters by 25% and informing targeted resource allocation.
Provided technical and strategic information support to the Walter Reed Military HIV Research Program, leading analytics across Kenya, Uganda, Tanzania, and Nigeria. Built an XGBoost time-series forecasting model achieving 93% accuracy for HIV viral load suppression projections through 2030. Deployed real-time dashboards in Tableau and Power BI, reducing reporting time by 25% and increasing program reach by 18%.
Served as Activity Manager for a $15M PEPFAR-USAID cooperative agreement, leading monitoring and evaluation, data quality assurance, DHIS2 dashboard development, and mid-term program evaluations. Improved quality reporting and evidence-based surveillance by 30% across supported PEPFAR programs in Nigeria, with analytical contributions reaching over 1 million beneficiaries.
Building AI-powered platforms (EpidPredict, SignalSifter, StrokeRisk AI, AlignInsight, RAG Assistant) serving practitioners at CDC, WHO, UNICEF, and Ministries of Health. Securing consortia for UNICEF, Gavi, and Wellcome Trust engagements.
AI platforms, research tools, and analytics systems deployed for global health practitioners, clinicians, and researchers.
An AI-powered knowledge companion that transforms complex global health guidance into instant, evidence-based insights. Grounded in WHO Strategic Information Guidelines, PEPFAR MER 2.6, and Nigeria DHS 2023-24 data. Eliminates hallucinations through retrieval-augmented generation over trusted official documents.
Launch App →An explainable AI prototype that transforms routine clinical and demographic data into personalized stroke risk predictions. Powered by XGBoost + SHAP + LLM Clinical Interpreter. Non-diagnostic screening tool for clinics, wellness programs, and public health researchers. 400+ user sessions in first week.
Try StrokeRisk AI →A three-layer red-teaming framework that stress-tests large language models for deceptive alignment and evaluation awareness across 10 healthcare vulnerability domains. Simple keyword filtering missed 83% of high-risk outputs. LLM semantic analysis achieved 100% detection with perfect expert concordance (κ=1.00). Published on medRxiv, January 2026.
Read Preprint →Nigeria has one of the highest burdens of zero-dose children globally, with only 39% fully immunized (basic antigens) and 31% receiving no vaccinations at all (NDHS 2024). This UNICEF/NPHCDA engagement (LRPS-2025-9199449) deploys three AI decision-support models, namely EpidPredict, SignalSifter, and time-series outbreak alerts, to shift immunization planning from reactive to proactive.
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