Championing Women Researchers

FATUMA ISSA, BSc

Ms Fatuma Issa is a data scientist at Ifakara Health Institute in the Biomedical and Clinical Trial Department in Bagamoyo, Tanzania

 

Fatuma Issa

research background

I am a statistician and a data scientist. My research journey began with hands-on involvement in managing and analysing clinical data for vaccine candidates for malaria (RH5 and whole sporozoite) and rabies. This experience provided deep exposure to real-world biomedical data, clinical protocols, and endpoint evaluations. Analysing data from these studies highlighted the importance of accurate, timely, and meaningful data interpretation in guiding medical decisions and shaping public health outcomes. This exposure also influenced my decision to pursue an MSc in Data Science to better understand the application of computational methods to extract insights from clinical and biomedical data. The programme has allowed me to build a strong foundation in machine learning, data engineering, and predictive modelling, with an emphasis on applications in healthcare and life sciences.

CURRENT research activities

My current research focuses on applying machine learning models to biomedical data. This was influenced by my MSc research, which focused on predicting cryptococcal meningitis among hospitalized patients. This project involves integrating clinical, laboratory, and demographic data to develop predictive tools that can support early diagnosis. The work aims to bridge the gap between computational modelling and clinical application by developing interpretable and robust models that can be integrated into hospital decision-support systems.

 

future research activities

Looking ahead, my research will continue to focus on biomedical science with a specific interest in vaccines, immunology, and infectious diseases. I aim to explore how immune system mechanisms can be modelled and understood through data. I am particularly interested in understanding vaccine responses such as hyporesponsiveness, pathogen interactions, and immune-mediated disorders using artificial intelligence and machine learning. I am also interested in combining -omics data, clinical records, and experimental outcomes to support biomedical research and improve healthcare delivery in low-resource settings.