Sahar Trabelsi

Ms. Sahar Trabelsi is a Ph.D. student in Applied Mathematics at the Institut Pasteur de Tunis and a dedicated member of the AMAX project team in Tunisia. Her research focuses on the development of adaptive, data-driven mathematical models to improve epidemic response strategies and optimize healthcare resource allocation, with a particular interest in equity and real-time decision-making in public health.

As part of the AMAX project, Sahar led a comprehensive study on the burden of COVID-19 hospitalizations across Tunisian regions. Her work examined regional and gender disparities in key hospitalization outcomes—including ICU and oxygen bed usage, mortality, and length of stay—across different COVID-19 variant waves. Using equity metrics such as the Gini coefficient and concentration indices, she assessed the fairness of hospital resource allocation. Her findings highlighted significant inequalities, particularly during the Delta wave, and provided actionable insights for more equitable healthcare planning in crisis settings.

Ms. Trabelsi holds a Master’s degree in Applied Mathematics with a specialization in Data Science and is currently pursuing her Ph.D. in Applied Mathematics, under the supervision of Professor Amira Kebir. Her doctoral research focuses on developing adaptive, data-driven models that support equitable epidemic response and optimize the allocation of healthcare resources. By integrating multilayer epidemic models, real-time data assimilation techniques, and optimal control strategies, her work addresses the dual challenges of disease dynamics and health equity.

Her areas of expertise include dynamic compartmental modeling, sensitivity analysis, data assimilation, equity evaluation methods, and optimal control. She also applies descriptive and statistical tools to public health datasets for policy-oriented research.

With a strong interdisciplinary background in mathematics and public health, Trabelsi contributes valuable insights to epidemic preparedness and health system strengthening, particularly in low- and middle-income countries.