Abebaw Gedef Azene

Trainee Type:

PhD

Research Topic:

Chronic complications among type 2 diabetes mellitus patients in Agincourt, South Africa: longitudinal follow up study.

Associated Institution:

Wits University, South Africa

Supervisor(s):

Prof Tobias Chirwa, Dr. Chodzidziwad Kabudula, Dr. Farzahna Mohamed

ORCID ID: 0000-0002-6374-546X

Abebaw Gedef Azene is a PhD fellow at the School of Public Health, University of the Witwatersrand (Wits University), supported by the SSACAB II program. His doctoral research focuses on survival data analysis.

Abebaw earned his MSc in Biostatistics from Bahir Dar University in 2016. Prior to beginning his PhD studies, he served as an Assistant Professor of Biostatistics at the School of Public Health, Bahir Dar University, Ethiopia, for five years. During this time, he taught various statistics-related courses. His primary research interests include spatial analysis and machine learning.

Alain Matazi Kangela

Trainee Type:

PhD

Research Topic:

Causal Modeling of Neglected Tropical Diseases' Dynamics in Sub-Saharan Africa: A Structural Equation Modeling Approach to Environmental and Socioeconomic Drivers

Associated Institution:

University of Abomey-Calavi, Benin

Supervisor(s):

Prof. Romain Glèlè Kakai

ORCID ID: 0000-0003-4779-7267

Alain Matazi Kangela is a Ph.D. candidate in Biostatistics at the University of Abomey-Calavi (UAC). He earned his master’s degree in Biomathematics and Applied Statistics, with a major in Biostatistics, from the same institution in 2022. Currently, he is affiliated with the Catholic University of Bukavu (UCB) in Bukavu, DR Congo, and is conducting his doctoral research at the Laboratoire des Biomathématiques et d’Estimations Forestières (LABEF) at UAC.

Kangela's expertise includes Biostatistics, causal modeling, agroecology, and pedometrics. His research encompasses a range of fields, including epidemiology (particularly disease modeling), spatiotemporal modeling of environmental variables using both Bayesian and classical inference techniques, as well as AI-based modeling of ecological and climate change. His doctoral thesis focuses on causal modeling to clarify the relationships between personal characteristics, environmental changes, and the dynamics of Neglected Tropical Diseases (NTDs) in sub-Saharan Africa.

Kangela has made significant contributions to the scientific community through his publications, which can be accessed at the following link: https://www.researchgate.net/profile/Alain-Kangela

Known for his proactive, versatile, and results-oriented approach, he applies his academic rigor and multidisciplinary expertise to tackle critical global health and environmental challenges.

Daniel Thoya

Trainee Type:

PhD

Research Topic:

Optimizing Child Mortality Reduction Strategies: A Machine Learning-Enhanced Adaptive Regression Discontinuity Approach

Associated Institution:

Moi University, Kenya

Supervisor(s):

Prof Ann Mwangi

ORCID ID: 0009-0004-8511-4898

Daniel Thoya is a dedicated statistician and a passionate PhD fellow at Moi University, Eldoret, Kenya. He brings extensive experience in research and statistical analysis, having served as a senior statistician with the County Government of Kilifi, Kenya.

Daniel holds a Master of Science in Applied Statistics from Jomo Kenyatta University of Agriculture and Technology (JKUAT) and a bachelor’s degree in applied Statistics with Computing from Karatina University. His strong academic background underpins his commitment to advancing statistical applications in public health.

Currently pursuing a PhD in Biostatistics, Daniel’s research explores the critical question: How can observational data be used to assess interventions where clinical trials are unethical or to evaluate ongoing public health programs? His work focuses on applying advanced statistical methodologies particularly adaptive regression discontinuity designs and causal inference techniques to generate robust evidence in contexts where randomized controlled trials (RCTs) are impractical.

With a deep commitment to ethical and impactful research, Daniel aims to bridge the gap between rigorous statistical methods and real-world health challenges. His goal is to provide policymakers and public health stakeholders with reliable, data-driven insights that inform effective and equitable interventions.

Cell; +254-729382835/735181709

Mail; danielthoya@hotmail.com

Edson Mwebesa

Trainee Type:

PhD

Research Topic:

Causal inference in absence of clinical trials

Associated Institution:

Moi University, Kenya

Supervisor(s):

Prof. Ann Mwangi and Dr John Ssenkusu

ORCID ID: 0000-0003-4421-5356

Edson Mwebesa is a PhD Biostatistics at Moi University, Eldoret, Kenya, and affiliated with Muni University, Arua, Uganda. In 2021, he earned his Master of Biostatistics degree from Makerere University, becoming the first and only graduate from the program's inaugural class under the SSACAB I fellowship. During his fellowship, his research focused on the determinants of maternal health services utilisation in Uganda using 2016 DHS data. Later, he contributed significantly to public health research in Uganda on the impact of antenatal care, malaria in pregnancy, maternal health service utilization, infectious diseases (COVID-19, TB, HIV) and impact evaluation of government programs among others. His research aims to enhance healthcare outcomes by addressing socioeconomic and cultural barriers to healthcare access and utilisation. His research is currently focused on causal inference in the absence of controlled trials, in hierarchical/clustered data structure, a common feature in observational data across fields.

Elysee KABONGO TSHIAMA

Trainee Type:

PhD

Research Topic:

The Impact of Climate Change on Malnutrition in Children Under 5 Years (0-59 Months) in The Democratic Republic of Congo Between 2014-2023. Bayesian Spatio-Temporal Analysis

Associated Institution:

University of Abomey-Calavi, Benin

Supervisor(s):

Prof. Romain GLÈLÈ KAKAÏ and Prof. Kandala Ngianga-Bakwin

ORCID ID: 0000-0001-9971-9389

Elysee KABONGO TSHIAMA is currently enrolled in a PhD in Biostatistics programme at the University of Abomey-Calavi, Cotonou, Benin with the sponsorship of the Sub-Saharan Africa Consortium for Advanced Training in Biostatistics (SSACAB II). Elysee holds a MSc in Biostatistics from the Institute of Medical Science (ISTM/Kinshasa), Democratic Republic of the Congo (DRC) with the sponsorship of SSACAB I. She completed her BSc degree in Community Health at ISTM/Kinshasa), DRC.

Previously, Elysee worked as Teaching and Research Assistant at the Department of Community Health, ISTM/Kinshasa, DRC with research interest in public health.

Elysee current research interest is in statistical modelling and geospatial analysis of large-scale household survey data combined with critical insight to the formulation of evidence-based policies in public health.

Evalyne Nyambura

Trainee Type:

PhD

Research Topic:

Characterizing the diversity of Plasmodium falciparum vaccine antigens and profiling antibody responses in naturally exposed individuals, in Webuye, western Kenya.

Associated Institution:

KEMRI-Wellcome Trust, Kilifi, Kenya

Supervisor(s):

Dr George Githinji

ORCID ID: 0000-0001-9971-9389

Evalyne Nyambura is currently pursuing a PhD at the KEMRI-Wellcome Trust Research Programme, where her research focuses on characterizing the diversity of Plasmodium falciparum vaccine antigens and profiling antibody responses in naturally exposed individuals in Webuye, western Kenya.

She holds a Bachelor of Science in Biochemistry (First Class Honours) from the University of Embu, awarded in September 2021. Following her undergraduate studies, she received a scholarship to pursue a Master of Science in Applied Microbiology at the same institution. Her MSc research was conducted at the U.S. Army Medical Research Directorate Africa, Basic Science Laboratory in Kisumu, Kenya, where she investigated the genetic diversity and antigenic variation of rabies viruses across various host species in Eastern and Western Kenya. She also collaborated with the Mpala Research Centre in Nanyuki on rabies surveillance efforts. Evalyne completed her MSc in September 2024 and transitioned directly into her PhD studies.

Her research interests span computational biology, biostatistics, and infectious diseases. She is particularly passionate about applying data-driven approaches to deepen the understanding of pathogen diversity and immune responses, with the aim of informing vaccine development and enhancing public health outcomes.

Evaristar Kudowa

Trainee Type:

PhD

Research Topic:

Integrative Data Analysis (IDA) for pneumococcal colonization studies, applying statistical methods to combine CHIM data from diverse settings

A Comprehensive Statistical Integration of Controlled Human Infection Study Data on Protection Against Pneumococcal Colonisation

Associated Institution:

University of Pretoria, South Africa

Supervisor(s):

Professor Samuel Manda & Dr. Marc Henrion

ORCID ID: 0000-0003-4505-5454

Evaristar Kudowa is a Biostatistician and PhD student in Biostatistics at the University of Pretoria. She holds a Master’s in Biostatistics from the University of Malawi and has worked with the Malawi Accelerated Research in Vaccines, Experimental and Laboratory Systems (MARVELS) consortium at the Malawi Liverpool Wellcome Programme. In collaboration with the Liverpool Experimental Human Pneumococcal Carriage (EHPC) team, she has focused on integrating Controlled Human Infection Model (CHIM) data from Malawi and the UK to optimize vaccine development.

Her research explores Integrative Data Analysis (IDA) for pneumococcal colonization studies, applying statistical methods to combine CHIM data from diverse settings. This work aims to advance data integration techniques for infectious disease and vaccine research.

Geoffrey Chiyuzga Singini

Trainee Type:

PhD

Research Topic:

Modelling and Forecasting for Multivariate Infectious Diseases using Regime Switching Time Series Models

Associated Institution:

University of Malawi

Supervisor(s):

Prof. Samuel Manda

ORCID ID: 0000-0001-7111-3000

Geoffrey Chiyuzga Singini is a seasoned Malawian professional with over 16 years of experience across the civil service, non-governmental organizations, and biomedical research institutions. His career spans diverse roles in education, development, and public health research, reflecting a strong commitment to advancing evidence-based solutions in these sectors.

Geoffrey has held key positions including Teacher, Monitoring, Evaluation, Accountability, and Learning (MEAL) Manager, and Senior Biostatistician at several esteemed institutions in Malawi. His academic background includes a bachelor’s degree in mathematical sciences education with a focus on Statistics and Computing, an MSc in Information Technology, and an MSc in Biostatistics.

Currently, Geoffrey is pursuing a PhD in Biostatistics as a proud SSACAB Fellow under the mentorship of Professor Samuel Manda. His doctoral research focuses on developing innovative models for multivariate data analysis and forecasting infectious diseases, work that aligns with SSACAB’s broader research agenda on biostatistical methods for data triangulation and evidence synthesis.

Driven by a passion for impactful research, Geoffrey is dedicated to applying advanced statistical methodologies to address complex public health challenges and support data-driven decision-making in Malawi and beyond.

Godwin Okeke Kalu

Trainee Type:

PhD

Research Topic:

Development and Evaluation of Artificial Intelligence Chatbot for Personalised Mental Health and Alcohol Literacy among University students in South Africa

Associated Institution:

University of the Witwatersrand, South Africa

Supervisor(s):

Prof Musenge Eustasius and Prof Joel Francis

ORCID ID: 0000-0002-3395-1625

Godwin Okeke Kalu is a public health researcher with a rich background in Epidemiology and Biostatistics, sharpened through academic excellence and extensive hands-on experience as one of the TDR scholars in 2020. With a Master of Science in Epidemiology with specification in Implementation Science from the University of the Witwatersrand, where he achieved a distinction for his research on malaria prevention in pregnant women, and a Bachelor of Science in Microbiology from the University of Ibadan, Godwin has consistently demonstrated his dedication to impactful research.

For past 6 years, his professional journey includes vital roles in HIV/AIDS programs, psychosocial support services and sexual rights advocacy, where he designed M&E frameworks, strategies, and develop grant proposals for improved access to different healthcare interventions targeting vulnerable populations (LGBTQI+, and sex workers) in Nigeria. As the Director of Research and Knowledge Management at The Initiative for Equal Rights (TIERs) for three years, Godwin led the completion of four research projects and successfully developed grant proposals and received grants worth over $500,000 for the organisations. These research projects include the national social perception survey of LGBTQI+ rights in Nigeria, Religious fundamentalism and evidence of sexual and diverse identities in precolonial in Nigeria and other community-based research and policy analysis aimed at eliminating sexual and gender-based violence and conversion practices.

His passion for applying statistical and analytical methods to health issues aligns seamlessly with his current pursuit of a PhD in Public Health in the field of Biostatistics as a SSACAB II fellow. Godwin’s doctoral research will employ a participatory approach to develop and validate data-driven tools to support mental health literacy, awareness and reduce rate of substance use among university students in South Africa. As part of engaging public in his research project, he aims to form a reference group of university students and mental health professionals to co-develop the data-driven tool aimed to personalise mental health messaging, and information on substance/alcohol use among young people in higher institutions.

Lameck Ondieki Agasa

Trainee Type:

PhD

Research Topic:

A Structural Equation Modeling Framework for Robust Instrumental Variable Estimation Using Sickle Cell Trait in Malaria and Vitamin A Deficiency Research

Associated Institution:

Moi University, Kenya

Supervisor(s):

Prof Ann Mwangi

ORCID ID: 0000-0002-3740-8865

Lameck Ondieki Agasa is a dedicated academician and researcher with extensive expertise in statistical analysis and research methodologies. He is currently pursuing a PhD in Biostatistics at Moi University, Kenya, and holds an MSc in Applied Statistics from Jomo Kenyatta University of Agriculture and Technology, as well as a BSc in Applied Statistics with Computing from Maasai Mara University. His research interests include Bayesian modeling, multivariate analysis, computational methods, and stochastic processes, with significant contributions to biomedical research, health education program evaluation, and the socio-economic factors influencing health outcomes.

Lameck has been actively involved in teaching, research supervision, and community outreach initiatives at Kisii University, where he serves as a Tutorial Fellow. His current research focuses on the spatial and causal modeling of malaria to enhance healthcare outcomes by addressing the socioeconomic and environmental factors influencing disease transmission. Specifically, he is investigating causal inference in observational data in the absence of controlled trials, with an emphasis on Mendelian randomization and the target trial framework for malaria.

Mary Magoya Ganya

Mary Magoya Ganya is a PhD candidate in Biostatistics at the University of Pretoria, specializing in spatial statistics. She holds a Master of Science degree in Biostatistics from Stellenbosch University and a bachelor’s degree in Statistics from the University of Malawi. Their research focuses on enhancing Geographically Weighted Regression (GWR) to incorporate non-parametric data, addressing key limitations in spatial modelling. With expertise in statistical analysis, geospatial methods, and health data analytics, Mary has applied their skills to various public health projects, including analysing malaria prevalence in children and assessing maternal and child health outcomes using DHS data. Her work aims to advance statistical methodologies for evidence-based public health decision-making in sub-Saharan Africa."

Midokpè Merveille Scholastique Essetcheou

Essetcheou is currently pursuing a PhD at the Laboratory of Biomathematics and Forest Estimation (LABEF) at the University of Abomey-Calavi (UAC). Her doctoral research focuses on the interactions between personal characteristics, environmental changes, and malaria dynamics in West Africa, utilizing Causal and Structural Equation Modeling (SEM). Through this work, she aims to contribute to the eradication of malaria and other epidemics by providing policymakers with robust, evidence-based tools for effective decision-making.

After earning her Master of Science in Biostatistics at UAC, Essetcheou continued her journey as a researcher at LABEF, where she actively contributed to various projects within the laboratory. These experiences enhanced her expertise in epidemiological modeling, data analysis, and collaborative research. Her master's thesis involved applying count time series models to analyze Lassa Fever data in Nigeria, a foundation that continues to influence her current research pursuits.

Essetcheou's undergraduate studies in Economic and Sector-Based Statistics at UAC included a thesis that analyzed the determinants of electricity generation in the West African Economic and Monetary Union region. This project demonstrated her ability to apply mathematical and statistical tools to tackle complex real-world challenges.

Professionally, she gained valuable experience through internships at SoBAPS S.A., SDCT, and SBEE, where she applied advanced statistical methods using tools like R, Python, and Stata to support data-driven decision-making in the health and energy sectors.

Essetcheou is also a co-author of a publication in the *African Journal of Applied Statistics* and has participated in notable initiatives such as FAO surveys and the *Data Science for Women in Africa* program. These experiences have solidified her passion for leveraging mathematical modeling to address pressing public health issues.

Mutale Sampa

Trainee Type:

PhD

Research Topic:

Predicting Infant Mortality Among Small Vulnerable Newborns in Relation to Maternal Risk Factors Using Machine Learning Techniques

Associated Institution:

University of the Witwatersrand, South Africa

Supervisor(s):

Prof Tobias Chirwa, Prof Wilbroad Mutale & Dr Justine Nasejje

ORCID ID: 0000-0001-5845-9533

Mutale Sampa is a PhD Fellow in Biostatistics at the University of the Witwatersrand, School of Public Health, located in Johannesburg, South Africa. She earned her master’s degree in medical Statistics from the University of Zambia under the prestigious SSACAB I scholarship. Currently, Mutale serves as a lecturer at the University of Zambia’s School of Public Health in the Department of Epidemiology and Biostatistics.

Her doctoral research focuses on maternal and newborn health, with a particular emphasis on neonatal mortality among Small, Vulnerable Newborns (SVNs). Through her work, Mutale aims to contribute to evidence-based strategies for improving health outcomes in this critical area.

Sarah Ogutu

Supervisor:

Prof. Henry Mwambi & Dr. Mohanad Mohammed

Research Topic:

Machine and Deep Learning Approaches for Analyzing High-Dimensional Survival and Longitudinal Data

Associated Institution:

University of KwaZulu-Natal

Sarah Ogutu is a PhD student in Statistics at the University of KwaZulu-Natal, South Africa. She holds an MSc in Statistics from the same university and has extensive experience lecturing and tutoring statistics courses. Her passion for health data led her to a research visit at Fred Hutch Cancer Centre, Seattle, USA, where she advanced her expertise in analyzing health-related datasets, particularly in the realm of vaccines and infectious diseases.

Sarah's current research combines advanced statistical methodologies and machine-deep-learning approaches to address challenges in high-dimensional survival and longitudinal data analysis. Outside of her academic pursuits, she enjoys sports, nature walks, and contributing to her community through mentorship and charity activities. She is committed to inspiring young minds and fostering academic excellence.