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.

Abebe Nega

Trainee Type:

PhD

Research Topics:

Statistical and machine learning methods for analyzing gene expression data.

Associated Institution:

University of Kwa-Zulu Natal

Supervisor(s):

Prof. Sileshi Fanta Melese, Prof. Henry Mwhambi, and Dr. Mohanad Mohammed

ORCID ID: 0009-0002-4322-7312

Abebe Nega holds an MSc in Biostatistics from Bahir Dar University, Ethiopia, and is currently a PhD fellow at the University of KwaZulu-Natal, South Africa. His doctoral research is conducted under the supervision of Prof. Sileshi Fanta Melese, Prof. Henry Mwhambi, and Dr. Mohanad Mohammed. Over the past four years, he has been actively involved in teaching and supervising undergraduate students in the Department of Statistics at Jimma University, Ethiopia. Through this work, he has developed strong academic and practical expertise in statistical methodology and its application to real-world problems.

His research interests lie in Biostatistics, with a focus on advancing statistical methodologies for biomedical data analysis. His previous work has applied rigorous statistical approaches to survival and longitudinal data, providing both theoretical insights and practical tools to support evidence-based decision-making. Currently, his research is expanding into statistical and machine learning methods for gene expression data analysis. His PhD project focuses on applying robust techniques to breast cancer gene expression profiles to identify prognostic gene signatures associated with disparities in survival outcomes between individuals of African ancestry and European American ancestry, as well as modeling the full transition pathways of cancer progression.

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.

Boboh Kamangira

Trainee Type:

PhD

Research Topics:

HIV Risk Prediction for Adolescent Girls and Young Women in East and Southern Africa: Developing and Validating a Machine Learning-Augmented Bayesian Spatial Model for Improved Precision, Accuracy and Targeted Interventions

Associated Institution:

Wits University, South Africa

Supervisor(s):

Prof Tobias Chirwa, Prof Latifat Ibisomi

ORCID ID: 0000-0002-5357-1013

Boboh Kamangirais is a PhD fellow in Epidemiology and Biostatistics at the School of Public Health, University of the Witwatersrand, South Africa, supported by the SSACAB II programme. His doctoral research focuses on advancing data-driven and equitable HIV prevention approaches for adolescent girls and young women in East and Southern Africa. He holds an MPhil in Demography and a Postgraduate Diploma in Health Economics from the University of Cape Town, and an Honours degree in Operations Research and Statistics from the National University of Science and Technology (Zimbabwe). He has over 10 years’ experience post masters in monitoring and evaluation, and health systems analytics through roles with the Clinton Health Access Initiative (CHAI) Malawi, OPHID Zimbabwe, and CORDAID Ethiopia, supporting data-driven decision-making across HIV and health systems programmes. His research interests include spatial and Bayesian analysis, machine learning in public health, infectious epidemiology and modelling, and health equity.

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.

Edson Mwebesa

Trainee Type:

PhD

Research Topics:

Causal inference in absence of clinical trials

Associated Institution:

Moi University, Kenya

Supervisor(s):

Prof. Ann Mwangi, 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 Topics:

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, Prof. Kandala Ngianga-Bakwin

ORCID ID: 0000-0002-7792-3984

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):

Prof. Wendy O’Meara, Prof. Francis Ndungu and Dr. Christine Markwalter

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.

Hillary Kibet Kiprono

Trainee Type:

PhD

Research Topic:

Influence of mobility in expansion and dispersal of infectious pathogens in Kenya

Associated Institution:

KEMRI-Wellcome Trust, Kilifi, Kenya

Supervisor(s):

Dr George Githinji, Prof Samson Kinyanjui, Prof Moritz Kreamer, Dr Ivy Kombe

ORCID ID: 0000-0003-1152-3213

Hillary is a PhD Fellow at the KEMRI-Wellcome Research Programme, Kilifi, Kenya supported by the SSACAB II program. His research focuses on integrating human mobility data (i.e. anonymized mobile phone data, air-passenger flight data and land-border crossing data) with genomics and epidemiological surveillance to investigate the routes of pathogen introduction/importations and the impact of mobility on local diffusion.

Hillary holds a Master’s degree in Biostatistics from Stellenbosch University (2022) and a Bachelor’s degree in Applied Statistics from Maseno University (2016). His areas of expertise includes Bayesian Spatio-Temporal modelling, clinical trials analysis and infectious disease modelling.

GitHub: https://github.com/Hillbett

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

Trainee Type:

PhD

Research Topics:

Flexible and Non-parametric Multivariate Geographically Weighted Regression

Associated Institution:

University of Pretoria

Supervisor(s):

Prof Samuel Manda, Prof Bedilu Alamirie

ORCID ID: 0000-0003-4574-2166

Mary Magoya Ganya Ganya is a PhD candidate in Mathematical Statistics 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. Her research focuses on developing a Flexible and Non-Parametric Multivariate Geographically Weighted Regression (FN-MGWR) framework that incorporates non-parametric smoothing and adaptive, variable-specific bandwidths. This methodological advancement enhances inference, improves model accuracy, and supports targeted public health interventions and evidence-based policy decisions.

With expertise in statistical analysis, geospatial methods, and health data analytics, Mary has applied her skills across various public health projects in both local and international organizations, including the Training and Research Unit of Excellence (TRUE), the African Institute for Development Policy (AFIDEP), and the Centers for Disease Control and Prevention (CDC) Malawi. Her work aims to advance statistical methodologies for evidence-based public health decision-making in sub-Saharan Africa.

Midokpè Merveille Scholastique Essetcheou

Trainee Type:

PhD

Research Topics:

Personal characteristics and environmental factors jointly shape malaria dynamics in West Africa

Associated Institution:

University of Abomey-Calavi (UAC), Benin

Supervisor(s):

Prof. Romain L. Glèlè Kakaï

ORCID ID: 0009-0004-7306-9962

Midokpè Merveille Scholastique Essetcheou is a Beninese biostatistician and PhD candidate in Biostatistics at the University of Abomey-Calavi, affiliated with the Laboratory of Biomathematics and Forest Estimation (LABEF). She holds a Master’s degree in Biostatistics and a Professional Bachelor’s degree in Economic and Sector-Based Statistics, with strong expertise in statistical modeling, data analysis, and applied research.

Her doctoral research focuses on understanding how individual characteristics, environmental factors, and interventions interact to influence malaria dynamics in West Africa. Using advanced causal and structural equation modeling approaches (CB-SEM and PLS-SEM), her work aims to generate robust, policy-relevant evidence to support malaria control and public health decision-making. She also studies antimalarial drug resistance in Benin using multilevel modeling frameworks.

In parallel with her academic work, she serves as a Technical Advisor in Statistics and Planning, contributing to survey design, data systems, monitoring and evaluation, and strategic planning for development and health programs. She has professional experience with organizations such as SoBAPS S.A., SDCT, SBEE, and ACCES UNIVERSEL, and is proficient in statistical software including R, Python, Stata, SPSS, and Excel.

She is a Fellow of the Individual Policy Exchange Programme (iPEP, 2025), a co-author of a peer-reviewed article in the African Journal of Applied Statistics, and a recipient of merit-based academic awards. Her work reflects a strong commitment to bridging research, policy, and practice to address public health challenges in Africa.

Mutale Sampa

Trainee Type:

PhD

Research Topics:

Predicting High-Risk Pregnancies and Neonatal Mortality Among Small Vulnerable Newborns In Lusaka, Zambia: A Machine Learning Approach for Targeted Antenatal Interventions

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.

Patane Shilabye

Trainee Type:

PhD

Research Topics:

Her PhD research investigates the role of HIV-related stigma, stress and cortisol, antiretroviral therapy (ART), and sleep quality in cardiovascular disease risk among people living with HIV. Using longitudinal data from the Ndlovu Cohort Study in Limpopo, South Africa, her work seeks to improve understanding of non-traditional and psychosocial risk factors for early vascular ageing.

Associated Institution:

University of the Witwatersrand, South Africa

Supervisor(s):

Prof. Kerstin Klipstein-Grobusch, Prof. Karine Scheuermaier, Dr. Alinda Vos-Seda (Co-supervisor)

ORCID ID: 0000-0001-7822-1792

Patane holds a BSc in Medical Sciences (majoring in Human Genetics) from the University of Limpopo, and a Master of Medical Sciences in Medical Biochemistry from the University of KwaZulu-Natal. She later obtained an MSc in Clinical Epidemiology from Utrecht University, where she also completed advanced coursework in infectious disease epidemiology, molecular epidemiology, clinical trials, and causal inference.

She has professional experience as a Junior Laboratory Assistant at the University of Limpopo and contributed to the COVID-19 school response programme at Lephadimisha Senior Secondary School, her former high school. She later worked as a Laboratory Demonstrator at the University of the Witwatersrand and as a student supervisor for medical students at Utrecht University. Alongside this, she has gained extensive experience in clinical and cohort research, statistical analysis using RStudio, and scientific writing.

She has published as first author in peer-reviewed journals and, in 2025, was awarded the Joep Lange Award at the INTEREST Conference in Windhoek, Namibia, for her oral presentation on HIV and cardiovascular disease risk.

Robson Dembah

Trainee Type:

PhD

Research Topics:

“Trajectories of health outcomes during the COVID-19 global pandemic among children in Soweto, Johannesburg, South Africa (2020–2022): a quantitative approach”, investigates how children’s health evolved during the pandemic. Using longitudinal data and trajectory modelling, his study examines patterns in health outcomes, the influence of socio-economic and environmental factors, and the role of healthcare access. The research also incorporates a machine learning component to enhance predictive modelling, with the aim of informing child health policies and resilience strategies in low-resource settings.

Associated Institution:

University of the Witwatersrand, South Africa

Supervisor(s):

Prof. Wiedaad Slemming, Dr. Roisin Drysdale

ORCID ID: 0009-0004-3527-7015

Robson has over five years of experience as a statistician/data analyst in research and program evaluations for NGOs, and more than four years in research and development as a statistician. His expertise includes descriptive and inferential statistics, regression modelling, Group-Based Trajectory Modelling (GBTM), and the application of advanced statistical software such as Stata. He has also worked on survey design, monitoring frameworks, and performance evaluations across diverse health and social sectors. His academic qualifications include advanced training in statistics and ongoing doctoral-level research.

Sarah Ogutu

Trainee Type:

PhD

Research Topics:

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

Associated Institution:

University of KwaZulu-Natal

Supervisor(s):

Prof. Henry Mwambi, Dr. Mohanad Mohammed

ORCID ID: 0000-0002-0166-8139

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.

Siyanda Madala

Trainee Type:

PhD

Research Topics:

Spatio-temporal analysis of climate change and health outcomes in rural South Africa, specifically examining climatological characteristics and their predictive value for adverse health outcomes among adults aged 18 and above between 1980 and 2024

Associated Institution:

University of the Witwatersrand, South Africa

Supervisor(s):

Prof Eustasius Musenge

ORCID ID: 0000-0001-6690-4326

Siyanda Madala is a PhD candidate in Biostatistics at the University of the Witwatersrand, South Africa. His research focuses on the spatio-temporal relationship between climate change and health outcomes in rural South Africa, with particular emphasis on prediction and description of health risks associated with environmental change. Siyanda’s work integrates advanced statistical modelling, spatial analysis, and machine learning to generate insights that can inform public health policy and adaptation strategies.

Siyanda is a PhD candidate in Biostatistics at the University of the Witwatersrand. His doctoral research focuses on the spatio-temporal analysis of climate change and health outcomes in rural South Africa, specifically examining climatological characteristics and their predictive value for adverse health outcomes among adults aged 18 and above between 1980 and 2024. His research combines statistical modelling, spatial analysis, and predictive machine learning approaches to strengthen understanding of how environmental change influences health, with the ultimate aim of informing public health policy and adaptation strategies.

Siyanda Madala has experience in Data Management at the Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, where he supports large-scale health research projects through advanced data management and statistical analysis. He previously worked as a Data Analyst and Statistical Consultant at SBIMB, providing biostatistical support to research teams and postgraduate students. He has also served as a Graduate Assistant at Wits, gaining experience in academic teaching, as well as a Tutor in Computing in Biostatistics and a Teaching Assistant in Epidemiology. He holds a BHSc honours and MSc from the University of the Witwatersrand.