Computational population health & precision prevention
Ebele Mogo, DrPH
ERIM Consulting
Research & Advisory
ERIM Consulting works at the intersection of public health science and computational methods, helping health systems and funders understand where prevention can have the greatest impact and what the evidence says about how to get there. Work spans three connected areas:
Understanding who is at risk and how
This work focuses on identifying populations where clinical, behavioral, and social risks compound in ways that drive preventable poor outcomes. It brings together public health subject matter expertise with computational and machine learning approaches, working with structured health system data including common formats like OMOP to identify the population patterns that matter most for prevention.
Understanding what works for those populations
This work draws on longitudinal and observational health system data to inform how preventive interventions are designed and how they can be optimized across different population subgroups in real-world settings, generating evidence that is both rigorous and decision-relevant.
From evidence to action
Drawing on extensive experience in evidence synthesis and data harmonization across varying national and provincial contexts, this work helps health systems and funders translate complex findings into investment decisions and prevention strategies, connecting rigorous analysis to real decisions, shaping how insights move from research into practice.
About Dr. Ebele Mogo
Dr. Ebele Mogo is a public health scientist working at the intersection of population health and computational methods. Her work focuses on how clinical and behavioral risks compound across populations, and how large-scale data and computational approaches can identify those at highest risk and determine which preventive interventions improve outcomes in real-world health systems with a particular focus on underserved populations.
Currently she serves as Principal Investigator on a federally funded research project applying computational methods to large-scale standardized clinical data across community health centers, identifying high-risk population subgroups and estimating intervention effects to guide more precise prevention strategies, with a focus on cardiometabolic disease in underserved populations.
Her scientific background spans behavioral health, evidence synthesis, and digital health across academic, startup, and global health contexts — including work with WHO, UNICEF, the Gates Foundation, Cambridge and McGill universities. She serves on the Board of the Campbell Collaboration and was on the inaugural steering group of Cambridge's Centre for Human-Inspired Artificial Intelligence.
Select Work
︎Causal ML for Syndemic Risk Quantification and Intervention Impact Forecasting
Leading a federally funded study applying machine learning and causal methods to EHR data from over 8 million patients across Federally Qualified Health Centers. Identifying syndemic risk clusters and estimating intervention effects for cardiometabolic disease in underserved populations. (Code and preprint forthcoming)
︎ NLP Applications for Public Health in Africa
Co-led a systematic scoping review mapping natural language processing applications across African health systems, identifying evidence gaps and opportunities for AI deployment in low-resource contexts.
︎ Sentiment Analysis of Preventive Behaviors During COVID-19
Analyzed social media engagement patterns around non-communicable disease prevention behaviors during lockdowns in Nigeria, generating insights on maintaining prevention messaging during public health emergencies.
︎ Mapping Health Resource Access Using Crowdsourced Data
Used crowdsourced digital platform data to map the relationship between neighbourhood deprivation indices and availability of health-promoting resources across Canadian cities — an early application of non-traditional data sources to understand spatial inequities in health resource access.
︎ Evidence & Gap Map — Inclusive Interventions for Children with Disabilities
Led production of a global evidence and gap map systematically mapping interventions for children with disabilities across low- and middle-income countries, informing UNICEF's Global Research Agenda and strategic investment priorities.