Agweng Fiona

Agweng Fiona is currently pursuing a master’s degree in Biostatistics at Makerere University’s School of Public Health. Her master's dissertation is titled "A Temporal Spatial Analysis of Climate Change and Respiratory Diseases in Uganda." In 2021, She graduated with a bachelor’s degree in business Statistics from Makerere University. Before starting her master's program, she worked as a Data Management Assistant.

Ann Kiplimo

Ann holds a bachelor’s degree in Applied Statistics with Computing from Karatina University in Kenya. Guided by her core values of respect, authenticity, and optimism, she is skilled in statistical software, database management systems, and data visualization tools. Previously, Ann worked as a Data Analyst at the Kenya Medical Research Institute, which specializes in clinical trials. She played a key role in conducting tuberculosis prevalence surveys for Nairobi County and was involved in the THANDYS (TB, HIV, and Dysglycaemia) study, contributing to the development of manuscripts for its initial phase. Currently, Ann is developing a custom solution using RStudio, with a focus on building a Shiny App to create a dynamic web application. Her project integrates predictive analytics and machine learning techniques for model training, highlighting her innovative approach to data-driven problem-solving.

Amalia Ndamanguluka Katuwapo Muhongo

Amalia Ndamanguluka Katuwapo Muhongo holds an honors degree in Applied Statistics from the Namibia University of Science and Technology. She is currently employed by the Oshikoto Regional Council as a Chief Development Planner, where she is responsible for compiling regional data and conducting assessments to inform regional development. Previously, she worked as a Regional Statistician for the Hardap Regional Council. Currently, she is enrolled at the University of Namibia, pursuing a Master of Science in Biostatistics. She has completed her coursework and is now working on the concept note for her thesis.

Her research topic is: **Utilizing Spatial and Temporal Analytical Methods to Assess and Mitigate the Impacts of Climate Variability and Change on Human Health and Socioeconomic Development in Africa.** Africa faces disproportionate vulnerabilities to climate variability and change, which are expected to intensify over the coming decades. The impacts of climate change on health, agriculture, water resources, and overall socioeconomic development pose serious challenges to the continent’s sustainable development efforts. Rising temperatures, variable precipitation patterns, and extreme weather events—such as droughts and floods—have led to adverse health outcomes and disrupted livelihoods, particularly in rural and low-income communities.

The primary objectives of her research are to:

• Apply spatial and temporal analytical methods to assess the impacts of climate variability and change on human health and socioeconomic development in Africa.
• Identify spatial hotspots and temporal trends related to climate-sensitive health outcomes and socioeconomic vulnerabilities.
• Develop tools and models that can support evidence-based decision-making for climate adaptation and resilience planning.

MINABA Sêgnimaké Tatiana Carine

MINABA Sêgnimaké Tatiana Carine earned her diploma in Life and Earth Sciences in 2022. She is currently pursuing a master’s degree in Biostatistics at the Laboratoire de Biomathématiques et Estimations Forestières (LABEF) within the Faculty of Agronomic Sciences (FSA) at the University of Abomey-Calavi (UAC). Her academic journey and professional experiences are deeply rooted in her passion for mathematics, biology, data science, and artificial intelligence. These fields inspire her to develop innovative solutions that address critical challenges in public health and social development.

Her research interests include data analysis, data science, and modeling. She is proficient in R, Python, and Advanced Excel. Her thesis focuses on evaluating the effectiveness of malaria dynamics in Sub-Saharan Africa by comparing two structural equation modeling approaches: Partial Least Squares (PLS-SEM) and Covariance-Based SEM (CB-SEM). This research examines how non-pharmaceutical interventions mediate the relationship between personal characteristics, environmental changes, and malaria dynamics, with the goal of improving public health strategies.

Tatiana is also an ambassador for the Women Artificial Intelligence and Data Academy in Benin. After completing her training, she worked on a project to design an AI-based tool for evaluating water potability. This tool allows for the quick verification of water safety based on measurable physicochemical parameters, contributing to enhanced food security and public health.

In addition to her academic pursuits, she has actively engaged in impactful projects. In September, she co-developed a chatbot for the e-service platform in Benin, simplifying administrative procedures for the population by guiding them on how to request essential documents such as secure birth certificates and national identity cards.

Most recently, she participated in the Blood Connect project with her team, designing a platform to optimize blood bank operations. This platform enables hospitals and patients to connect, ensuring that individuals in need of urgent blood transfusions can quickly locate available resources at other facilities. This initiative reflects her commitment to using data and technology to address healthcare disparities.

Dovonou Houétchénou Gislain Fortuné

Trainee Type:

Master

Associated Institution:

University of Abomey-Calavi, Benin

Dovonou holds a Bachelor's degree in Agroeconomics, Sociology, and Rural Extension, which he obtained in 2022 from the University of Abomey-Calavi in Benin. His research focuses on “Causal and Structural Equation Modeling Applications in Epidemiology: Insights and Review of Malaria-Related Studies.” This work aims to explore how causal modeling and structural equation methods can offer innovative insights into studies related to malaria epidemiology.

He is passionate about advancing his career as a biostatistician, particularly in applying statistical and interdisciplinary methods to address pressing global issues, such as malaria epidemiology and the impacts of climate change on vulnerable populations. Currently, he is honing his expertise in data manipulation, advanced modeling techniques, and complex epidemiological analyses to tackle these multifaceted challenges and contribute to evidence-based solutions.

Supervisor(s):

• Prof. dr. ir. Romain Lucas GLELE KAKAÏ

Evelina Natangwe Sakeus

Associated Institution:

University of Namibia

Research Topic:

Using Machine Learning Approaches to Enhance Population-Based Prevention of, and Personalized Treatment for, Communicable and Non-Communicable Diseases in Namibia

Sakeus earned a Bachelor of Science with Honours in Statistics from the University of Namibia in 2019. During her undergraduate studies, she served as a Student Assistant Tutor in the Department of Statistics and Population Studies from July 2016 to October 2018, where her primary responsibility was to tutor first- and second-year students in various statistics modules.

After graduating, Sakeus interned as a Monitoring and Evaluation Officer at the Society for Family Health, a non-governmental organization. Currently, she is pursuing a Master of Science in Biostatistics at the University of Namibia. Her research focuses on using machine learning approaches to improve population-based prevention and personalized treatment for both communicable and non-communicable diseases in Namibia. Through her work, she aims to develop a predictive model that enhances these health strategies.

Itesiwajuayo Babalola

Itesiwajuayo Babalola is currently pursuing a master’s degree in advanced data analytics at the University of Pretoria. She has completed her coursework and has gained a deeper understanding of various data analysis techniques. She will continue to focus solely on the research component of her degree, planning to investigate the use of mixture modeling in meta-analysis, particularly in the context of heterogeneity. In 2022 and 2023, she graduated with both a BSc and a BSc Honours in Mathematical Statistics from the University of Pretoria. Itesiwajuayo is deeply inspired by the potential of biostatistics to contribute to evidence-based medicine and public health interventions. Her research aims to apply methods derived from mixture modeling to health data, demonstrating how these techniques can address challenges in applied health research.

Memory Makuta

Memory Makuta holds a Bachelor of Science degree in Mathematical Science and is currently pursuing a Master of Science in Biostatistics at the University of Malawi. She works as a Monitoring, Evaluation, and Learning Specialist at One Acre Fund. With over seven years of experience in quantitative data collection and analysis, she specializes in cleaning and analyzing data using STATA. In her current role, she oversees data collection, manages various projects, leads teams, trains staff on data quality management, and ensures team performance. Memory is passionate about quality data and believes that a well-trained team is essential for accurate data collection. She is also working on a research project titled "Assessing Methods for Detecting Outliers in Meta-Analysis" which involves a simulation study and illustrative examples. In this project, she is evaluating several outlier detection models and comparing their effectiveness in identifying outliers.

Nabirye Phiona Milly

Nabirye Phiona Milly is currently pursuing a Master's degree in Biostatistics at Makerere University, with her research focusing on the intersection of climate change and nutrition. With 10 years of experience in data management, Phiona has worked on numerous projects that involved handling large volumes of information. This extensive experience has made her a data expert, capable of managing various types of data sets. Phiona is now looking to advance her career by learning how to convert raw data into meaningful insights. Her goal is to apply this knowledge to significant research in public health, medicine, and genetics.

Sydney Sambo

Associated Institution:

University of KwaZulu Natal

Sambo is a master’s candidate at the University of KwaZulu-Natal. He holds a Bachelor of Science Honours degree in Statistics, as well as a Bachelor of Science in Statistics and Applied Mathematics. Currently, he is working on his master’s research in Biostatistics, focusing on the spatial distribution and determinants of malaria among children under five years old in Liberia. He has a strong interest in randomized controlled clinical trials, tropical neglected diseases, infectious disease modeling, epidemiology, and statistical programming.

Supervisor(s):

• Professor Faustin Habyarimana
• Professor Shaun Ramroop

Fredrick Orwa

I am a knowledgeable Statistician with over four years of progressive experience analyzing data related to health research. My primary interest is in using observational data to assess the impact of interventions, particularly in public health and epidemiology.

In my professional journey, I have worked extensively with various research experts and have developed a robust understanding of statistical concepts, methods, and models. Previously, at KEMRI-Wellcome Trust, I served as an Assistant Research Officer-Statistics, where I successfully led several projects, including analyzing invasive pneumococcal disease data using interrupted time series to assess the impact of the ten-valent pneumococcal conjugate vaccine on invasive pneumococcal disease. This analysis demonstrated a 94% reduction in invasive pneumococcal disease cases among children under five years old in Kenya.

I have strong technical expertise in statistical programming and have been using R and Stata for my data analyses. Beyond my technical skills, I am adept at working both independently and as part of a team, with strong analytical and critical thinking abilities. My commitment to excellence in research and data analysis is driven by a passion for improving health outcomes through evidence-based interventions.

I am thrilled about this new academic opportunity provided by SACCAB in collaboration with Moi University and am eager to make further contributions to the fields of biostatistics and public health research.

Isaac Waluke Kundu

Associated Institution:

University of Nairobi

Department:

Mathematics

Course:

Data Science (Public Health) SSACAB Attached Institution: KEMRI-Wellcome Trust

City:

Nairobi-Kenya

Isaac is a proficient Data Scientist with a strong background in statistics and mathematical analysis. He earned his BSc in Applied Statistics from Kisii University before advancing to an MSc in Mathematical Statistics at the same institution. His academic journey took a remarkable leap when he was awarded the prestigious APHREA-DST scholarship, enabling him to pursue an MSc in Data Science (Public Health) at the University of Nairobi. This opportunity allowed him to refine his expertise in Machine Learning, Deep Learning, and Big Data analytics.

His dedication to research earned him further recognition through a scholarship from the Sub-Saharan Consortium for Advanced Biostatistics Training (SSACAB). This opportunity facilitated his impactful research at KEMRI-Wellcome Trust, where he explored the application of NLP models for mapping free-text medical diagnoses and treatments to their corresponding ICD-11 codes.

With certifications in SPSS, Python, and R, Isaac is highly skilled in leveraging AI to tackle complex challenges. He is driven by the philosophy that "Data is the nutrition of AI," continuously pushing the boundaries of innovation in data science and public health.

Bongani Ncube

University of Witwatersrand

School of Public Health

Division of Epidemiology and Biostatistics

Bongani is a dedicated data scientist and mathematician, He holds a Bachelor of Science with Honours in Statistics from the University of Zimbabwe.

He brings expertise in statistical modelling and predictive analytics, blending mathematical rigor with real-world applications.

With a keen interest in Bayesian statistics, spatial statistics, and time-to-event studies for health research, Bongani's work focuses on improving lives. His current research explores mapping hotspots for Type 2 Diabetes Mellitus (T2DM), leveraging spatial statistics and machine learning to uncover critical insights for better healthcare planning.

Driven by a passion for education, Bongani actively champions machine learning and statistical learning, especially for newcomers. He adores working with the Tidyverse and Tidymodels in R, making data science not just accessible but also exciting. Through his thoughtful blogs, he demystifies complex concepts and shares his learning journey with a broad audience. Explore his insights and experiences on his website: bongani-ncube.netlify.app.

Brian Masafu

Affiliation:

KEMRI-Wellcome Trust Research Programme, MSc. Research SSACAB II Fellow

Research Title:

Assessing the Impact of PCV for Sustaining its Benefits on Pneumococcal carriage and Invasive Disease in Ethiopia

Supervisor:

Dr. John Ojal – Kemri-Wellcome Trust Research Programm

Brian Masafu is an MSc Research Fellow at KEMRI-Wellcome Trust Research Programme, specializing in statistical and mathematical modeling of infectious diseases. With a background in molecular biology, bioinformatics, and epidemiology, his research focuses on using dynamic transmission models to assess the impact and benefits of introducing the pneumococcal conjugate vaccine (PCV) in the Ethiopian population. By leveraging nasopharyngeal carriage data and mathematical modeling, he provides a cost-effective alternative to resource-intensive surveillance systems for measuring vaccine impact.

His research interests are in Infecious disease Epidemiology, Disease Modelling, vaccinology and Health Policies.

Brian is a proud recipient of the SSACAB II fellowship, which has supported his research endeavors.