SSACAB > Co-applicants

Co-applicants

Dr. Sam Kinyanjui

KEMRI-Wellcome Trust Programme, Kenya

Dr. Sam Kinyanjui is the Head of Training and Capacity Building at the KEMRI-Wellcome Trust Programme in Kenya and the Director for the Initiative to Develop African Research Leaders (IDeAL). Prior to current position he spent 16 years doing research on the immunology and molecular biology of malaria parasites. During this period he developed a strong interest in capacity building for health research in Africa.

As the Head of Training and Capacity Building at the KWTRP in Kenya he provides scientific, and strategic guidance for academic training towards research leadership. His key achievement has been the development and implementation of a comprehensive research career framework for attracting, training and retaining African research leaders. Through the framework Dr. Kinyanjui has overseen the training of over 200 graduate interns, the majority of who have taken up a research career after the internship. This scheme has now been developed into a nationally accredited Postgraduate Diploma in Health Research Methods. He has also overseen over 90 Masters and over 60 PhD training since 2008. In 2015 Dr. Kinayanjui was awarded a further 8 million pounds by the Wellcome Trust to build on this work through the Initiative to Develop African Research Leaders (IDeAL)

Prof Henry Mwambi

University of KwaZulu Natal

Prof Henry Godwell Mwambi is a co-applicant for Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB)Training programme. He is an Associate Professor at the School of Mathematics, Statistics and Computer Science, University of Kwazulu Natal. He has taught both theory and applied statistics at undergraduate and post-graduate levels. His main application areas are in the biological and health sciences particularly modelling population and disease dynamics. Prof Mwambi's main research area is on statistical and mathematical modeling and analysis of infectious disease processes at the individual and population level. He currently works with PhD and Masters students on various topics in biostatistics and epidemiology such as the analysis of non-Gaussian longitudinal and clustered disease outcome data, survival analysis, modelling recurrent event longitudinal data with reference to epilepsy, and infectious disease modelling.

Jim Todd

London School of Hygiene and Tropical Medicine

Jim Todd is Professor of Applied Biostatistics in London School of Hygiene and Tropical Medicine (LSHTM) and a co-founder of the INSPIRE network. INSPIRE aims to bring together African data professionals to build a practical network for harmonizing and sharing data on population health in East Africa. He has worked with several health and demographic surveillance systems (HDSS) over the last 32 years, initially working with INDEPTH. He and Prof Basia Zaba (LSHTM) used that experience to form the ALPHA network to analyse harmonized population data on HIV. Jim and Damazo Kadengye (APHRC) formed the INSPIRE network led by an African institution focussed on eastern African population data, using new techniques and methods to expand the harmonization of data to other diseases and conditions.

Jim is focussed on training African biostatisticians and developing opportunities for them to expand their work. With Tobias Chirwa (Wits) he is co-PI of the SSACAB consortium, and has worked with several of the SSACAB institutions to help establish Master’s programs in epidemiology and applied biostatistics. The aim is to build the cadre of data professionals to utilise the opportunities for analysis in the future, building on the wealth of health data in African countries

Prof Romain Glèlè Kakaï

University of Abomey-Calavi

Romain Glèlè Kakaï is a Professor of Biometry and Coordinator of the Master Programme in Biostatistics and PhD programme in Biometry at the University of Abomey-Calavi (Benin). He is the head of the “Laboratoire de Biomathématiques et d’Estimations Forestières” and Director of the Humboldt Research Hub on Socio-Ecological and Spatio-temporal Modeling of COVID-19 in Africa. He is also a Malaria modeling expert at the Vaccine Impact Modeling Consortium, funded by GAVI and the BMGF. He was an Alexander von Humboldt research fellow in Biometry at the University of Freiburg (Germany, 2007-2008). He is a guest lecturer in Biostatistics in Ghana, Cote d’Ivoire, Togo and Niger. He has supervised eight PhD and more than 45 MSc students in Biometry/Biostatistics and has co-authored more than 200 peer-reviewed journal articles in Statistics and Forest modeling. His current statistical interest is Infectious disease modeling and AI and Machine Learning in Health and Agriculture.

Prof Daniel Oberski

Utrecht University

Daniel Oberski is a Full Professor of health and social data science at the department of Methodology & Statistics, Utrecht University, the Netherlands. He is also the Head of the Department of Data Science at the Julius Center, University Medical Center Utrecht (UMCU), the Netherlands. Daniel is a member of UMCU's Digital Health team, which is responsible for implementation of AI innovations in the hospital. He has worked on latent variable models, unsupervised learning, structural equation models, and applications of machine learning to health.

Prof Samuel Manda

University of Pretoria

Samuel Manda is a full Professor and Head of the Department of Statistics at the University of Pretoria, Pretoria, South Africa. Professor Manda obtained his BSc (Hons) in Mathematics from the University of Malawi, MSc in Mathematical Statistics from the University of Sheffield, and PhD in Bayesian Statistics from the University of Waikato. Subsequently, he did postdoctoral studies in nonparametric Bayesian survival statistics at the University of Auckland, New Zealand. He has served as Director of the Biostatistics Research Unit at the South African Medical Research Council, South Africa. Previously, Prof Manda was at the University of Leeds, United Kingdom. His main research areas have been in the development and application of Bayesian multivariate spatial models, and advanced statistical methods for multivariate survival data. However, he has recently been working on some problems in joint models of longitudinal and time-to-event data; estimation issues in causal inference, and topics in data and evidence integration. Prof Manda has over 160 referred publications and co-authored/co-edited 12 books/chapters on biostatistics methods and applications. He is a National Research Foundation-rated scientist and is a Visiting Professor in Statistics at the University of Malawi and holds Extraordinary Professorships at the Universities of Stellenbosch and an Honorary Research Professor at the University of Kwazulu-Natal. Prof Manda’s science contributions also cover postgraduate supervision and membership of national and international technical committees. Over the years, he has supported several major health projects funded by major funding agencies including World Health Organisation, UNICEF, Global Fund, UK Medical Research Council, the Centres for Disease Control and Prevention, and United Nations Children's Fund (UNICEF). He has supervised over 35 MSc and Ph.D. students in Biostatistics and is Co-Principal Investigator (CoPI) for the DELTAS Africa Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) and the eR-BioStat International Training Program (ITP): Development of Local E-learning Platforms in (Bio)Statistics.

Prof Ann Mwangi

Moi University

Ann Wanjiru Mwangi holds a PhD in Biostatistics from Brown University, USA, masters’ degrees (Biostatistics and Applied Statistics) from Hasselt University, Belgium and Bsc from JKUAT, Kenya. She is an Associate Professor of Biostatistics and Associate Dean in charge of Research and Innovation, Moi University Kenya. She is involved in training and supervising undergraduate and graduate students in the School of Science and College of Health Sciences. She has been a Biostatistician for the Academic Model Providing Access to Health Care (AMPATH) program for the last 16 years during which several papers have been published in peer-reviewed journals. Her research interests are in Data Science, Causal Inference, methods for addressing bias when using observational data in resource limited settings in HIV & AIDS, Chronic Diseases among others. She is a Co-Investigator on several funded grants, including a D43 & DSI training grant, an R01 to optimize HIV treatment and R01 on Data Science for Clinical decision support that aims to utilize big data to develop a clinical decision support to improve retention and viral suppression in western Kenya among others within AMPATH. She has extensive experience in Quantitative Research Methods, Grant writing, Manuscript writing and Data Analysis.