Abebaw Gedef Azene

Trainee Type:

PhD

Associated Institution:

Wits University, South Africa

Supervisors:

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

Abebaw Gedef Azene is a PhD fellow in the School of Public Health, Wits University by the sponsor of SSACAB II. He is working on his PhD dissertation on survival data analysis. Abebaw completed his MSc. at Bahir Dar University in 2016. Before commencing a PhD fellowship, Abebaw worked as an assistant professor of Biostatistics for 5 years at the School of Public Health, Bahir Dar University, Ethiopia. His teaching profile includes several courses in the areas of statistics. His research interest is spatial analysis and machine learning.

Alain Matazi Kangela

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 links: http://orcid.org/0000-0003-4779-7267, http://www.webofscience.com/wos/author/record/HNP-0761-2023, and 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.

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.

Evaristar Kudowa

Research Group/Department:

Statistical Support Unit

Supervisory Team:

Professor Samuel Manda & Dr. Marc Henrion

Associated Institution:

University of Pretoria, South Africa

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.

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.

Edson Mwebesa

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.

Souand Peace Gloria TAHI

Souand Peace Gloria TAHI is a biostatistician, data analyst, and AI researcher. A recipient of DAAD, AI4D, and Mawazo scholarships, she conducted her PhD at the Laboratory of Biomathematics and Forest Estimation (LABEF), where she developed machine learning models to optimize maize yield prediction in Benin, integrating climate, soil, and remote sensing data.

Currently a postdoctoral researcher at LABEF, she uses structural equation modeling to explore the complex interactions between environmental, socioeconomic, and demographic factors influencing tropical disease patterns in Sub-Saharan Africa. Through her multidisciplinary research, she aims to bridge AI, agriculture, and public health for sustainable development in Africa.

Geoffrey Chiyuzga Singini

Supervisor:

Prof. Manda

I am Geoffrey Chiyuzga Singini, a Malawian professional with over 16 years of diverse experience spanning civil service, and non-governmental organizations including biomedical research institutions. My career trajectory reflects a blend of roles in education, development work, and public health research institutions. I have served as a teacher, a Monitoring, Evaluation, Accountability, and Learning (MEAL) Manager, and a Senior Biostatistician across various esteemed institutions in Malawi. Academically, I hold a Bachelor’s degree in Mathematical Sciences Education, with a focus on statistics and computing, an MSc. degree in Information Technology, and an MSc. degree in Biostatistics. Currently, I am pursuing doctoral studies in Biostatistics as a proud SSACAB fellow, under the mentorship of Professor Samuel Manda. My PhD research is dedicated to developing novel models for multivariate data analysis and forecasting infectious diseases. This critical work aligns with the broader SSACAB’s research theme of exploring biostatistical methods for data triangulation and evidence synthesis. Driven by a passion for impactful research, I am committed to leveraging statistical methodologies to address complex challenges in public health and contribute to evidence-based decision-making in Malawi and beyond.