Modelling, Inference, and Data Science
Betrand Fesuh Nono
Dr. Betrand Fesuh Nono is an accomplished statistician and postdoctoral researcher in the Division of Biostatistics and Health Data Science at the University of Minnesota. He currently serves as the Group Leader for the Modelling, Inference, and Data Science (MIND) Research Group at the Translational Research and Development Foundation (TREND Foundation). He holds a PhD in Applied Statistics from the University of Yaoundé I, Cameroon, and has dedicated his career to advancing statistical frameworks that address complex public health challenges.
Dr. Fesuh Nono specializes in multilevel modelling, high-dimensional data analysis with measurement error handling, GWAS, microarray gene expression processing and analysis, missing data, latent class analysis. His technical proficiency in R and Python allows him to bridge the gap between complex theory and actionable data visualization through interactive dashboards. His current research interests include areas like biomarkers of neurodevelopmental disorders and cancer, latent class analysis for comparing diagnostic tools without gold standard. Throughout his career, Dr. Fesuh Nono has led impactful initiatives, such as evaluating insecticide resistance in malaria vectors to refine control strategies in Cameroon. Beyond his methodology work, he is a committed mentor to junior researchers and an active member of the Cameroon Statistical Society (SOCASTAT). With a focus on leadership and innovation, he aims to pioneer data-driven public health interventions that enhance health equity across Africa and beyond.
Background
The increasing complexity of contemporary public health and environmental challenges demands new approaches that combine quantitative sciences with biological, epidemiological and ecological research. Emerging and re-emerging infectious diseases, antimicrobial resistance, the growing burden of non-communicable diseases, climate change, biodiversity loss and food insecurity are interconnected phenomena that transcend disciplinary boundaries and require integrated analytical frameworks. At the same time, unprecedented growth in the availability of epidemiological, clinical, genomic, demographic, geospatial and environmental data presents new opportunities to better understand these complex systems. Harnessing these data, however, requires advances in mathematical modelling, statistical inference, artificial intelligence and computational data science capable of transforming large, heterogeneous datasets into actionable scientific evidence. Increasingly, public health decision-making depends not only on data availability but also on the ability to infer causal relationships, quantify uncertainty, predict future scenarios and evaluate the likely impact of interventions. Mathematical and computational models have therefore become indispensable tools for understanding disease transmission, forecasting outbreaks, designing surveillance systems, optimising resource allocation and informing evidence-based policy. Equally, the growing recognition of the interconnectedness of human, animal and environmental health has reinforced the need for multidisciplinary approaches grounded in the One Health framework, where quantitative methods support integrated surveillance, ecological risk assessment and the management of complex socio-ecological systems. It is within this scientific context that the Modelling, Inference and Data Science (MIND) Research Group was established at the Translational Research and Development Foundation (TREND Foundation). The Research Group provides a multidisciplinary platform for developing and applying innovative methodologies in mathematical modelling, statistical inference, computational epidemiology, machine learning and data science to address pressing challenges in global health and sustainable development. By integrating quantitative methods with expertise in epidemiology, microbiology, genomics, environmental sciences and public health, MIND Research Group seeks to generate robust evidence that advances scientific understanding, supports policy development and accelerates the translation of research into effective health interventions.
Research Goals
The overarching mission of the MIND Research Group is to advance translational quantitative research that informs policy, strengthens health systems and improves population health through rigorous modelling, inference and data-driven innovation. The MIND Research Group aims to develop novel mathematical, statistical and computational methodologies capable of addressing emerging and persistent challenges in infectious diseases, environmental health and global health security. These methodological advances provide the analytical foundation for forecasting disease outbreaks, quantifying transmission dynamics, estimating intervention impact, assessing uncertainty and supporting evidence-based resource allocation. MIND Research Group is equally committed to strengthening integrated surveillance and decision-support systems by combining advanced analytics with multidisciplinary expertise. The group develops scalable frameworks for disease monitoring, early warning, spatial risk mapping, causal inference, predictive modelling and health systems evaluation, enabling governments and public health agencies to anticipate, prevent and respond effectively to evolving health threats. By incorporating environmental, climatic, demographic and socio-economic drivers into analytical frameworks, MIND Research Group contributes to a more comprehensive understanding of the determinants of health and disease across diverse settings. Research within MIND Research Group is inherently collaborative and translational. The group works closely with researchers across the TREND Foundation, universities, ministries of health, national disease control programmes, regional public health institutions and international research partners to co-design studies, strengthen analytical capacity and translate scientific discoveries into policy and practice. Through these partnerships, MIND Research Group contributes to the development of evidence-based guidelines, surveillance strategies, elimination programmes and health policies that address local priorities while contributing to regional and global health agendas. Ultimately, the MIND Research Group strives to establish a centre of excellence in quantitative and computational health sciences by advancing methodological innovation, promoting open and reproducible science, strengthening interdisciplinary research capacity and generating high-quality evidence that accelerates disease elimination, improves health equity and enhances the resilience of communities facing emerging public health and environmental challenges.
The research activities of the Modelling, Inference and Data Science (MIND) Research Group are organised around four complementary research axes that integrate mathematical modelling, statistical inference, epidemiology and data science to address priority challenges in global health. Together, these research axes provide a multidisciplinary framework for generating high-quality evidence, advancing methodological innovation and translating research findings into effective public health policies and interventions.
Research Axis #1 - Quantitative and applied epidemiology
This Research Axis focuses on the development and application of advanced quantitative methods to better understand the distribution, determinants and burden of disease. Integrating statistical inference, biostatistics, causal inference, machine learning and computational epidemiology, the group analyses complex health datasets to identify risk factors, estimate disease burden, evaluate intervention effectiveness and strengthen disease surveillance systems. Research under this Research Axis supports evidence-based public health decision-making through robust analytical frameworks, predictive analytics and the generation of high-quality epidemiological evidence for national and international health programmes.
Research Axis #2 - Transmission dynamics of infectious diseases
Understanding the mechanisms that govern disease transmission is fundamental to the prevention, control and elimination of infectious diseases. This Research Axis investigates the biological, environmental, behavioural and socio-demographic determinants that shape the emergence, persistence and spread of pathogens within and between populations. Using epidemiological analyses, mathematical models and spatial-temporal data, researchers quantify transmission patterns, identify drivers of infection, estimate key epidemiological parameters and evaluate factors influencing disease persistence. Particular emphasis is placed on malaria, onchocerciasis and other neglected tropical diseases, while maintaining the flexibility to address emerging infectious disease threats.
Research Axis #3 - Modelling and simulation of infectious diseases
Mathematical modelling provides a powerful framework for understanding disease systems and evaluating alternative intervention strategies before implementation. This Research Axis develops deterministic, stochastic, agent-based and hybrid models to simulate disease transmission dynamics and assess the effectiveness, efficiency and cost-effectiveness of public health interventions. By integrating epidemiological, demographic, environmental and genomic data, the group generates predictive models that inform outbreak preparedness, disease elimination strategies, resource optimisation and health policy planning under diverse epidemiological scenarios.
Research Axis #4 - One Health modelling and integrated systems science
Recognising that human, animal and environmental health are intrinsically interconnected, this Research Axis applies quantitative modelling within the One Health framework to address complex health challenges at the human-animal-environment interface. Research encompasses zoonotic diseases, antimicrobial resistance, integrated surveillance, ecosystem health, biodiversity conservation and the impacts of climate variability and environmental change on disease ecology. By integrating ecological, veterinary, environmental and public health data, MIND Research Group develops systems-based models that support cross-sectoral decision-making, strengthen preparedness for emerging health threats and promote sustainable approaches to health and environmental resilience.
Dr. Betrand Fesuh Nono
Emanuele Giorgi
Professor of Statistical Science in the Department of Applied Health Sciences at the University of Birmingham and Honorary Professor in the Faculty of Health and Medicine at Lancaster University, UK
Dr. Claudio Fronterre
Senior Statistical Scientist in the Department of Applied Health Sciences at the University of Birmingham, UK, specialising in spatial and spatio-temporal statistical modelling for global health, with a particular focus on neglected tropical diseases and other health challenges in low-resource settings.
Dr. Betrand Fesuh Nono
Group Leader
fesuhnono@trendfoundation.org
Dr. Rogers Nditanchou
Deputy Group Leader
nditanchou@trendfoundation.org