Mathematical and statistical modeling in oncology is a multidisciplinary field that applies mathematical and statistical techniques to understand, describe, and predict various aspects of cancer biology, epidemiology, and treatment. It plays a crucial role in advancing our understanding of cancer, optimizing treatment strategies, and informing healthcare decision-making. Some key areas of research within this field include: developing mathematical models to describe how tumors grow, evolve, and spread within the body, quantifying the behavior of cancer drugs in the body and optimizing treatment strategies; studying the incidence and prevalence of cancer, identifying risk factors, and predicting cancer trends; tailoring cancer treatment plans to individual patients based on genetic and clinical data; designing statistically rigorous clinical trials to evaluate the effectiveness of new cancer treatments.
The school is designed to provide participants with in-depth knowledge and skills in the application of mathematical and statistical techniques to cancer research. The goal is to equip participants with the tools and expertise necessary to address complex problems in oncology using quantitative methods. It aims to contribute to bridge the gap between mathematics, statistics, and medicine to advance our understanding of cancer and improve patient outcomes.
Participants of the school can expect to gain a deep understanding of both the theoretical foundations and practical applications of mathematical and statistical techniques in the context of cancer research. Here are some specific learning outcomes. Participants will learn how to formulate mathematical models to describe cancer-related processes, such as tumor growth, metastasis, and treatment response. They will acquire expertise in statistical methods for analyzing clinical and experimental data, including survival analysis, regression analysis, and hypothesis testing. They will learn how to integrate data from various sources, such as genomics, imaging, and clinical records, to gain insights into cancer biology and patient outcomes. Proficiency in programming languages commonly used in mathematical and statistical modeling, such as R, Python, will be taught to implement and simulate models.
Understanding the clinical and biological context of cancer research is crucial, so participants will gain knowledge of cancer biology and relevant medical terminology.
Collaboration skills will be emphasized, as participants will likely work with researchers from diverse backgrounds, including oncologists, biologists, and statisticians.
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Le programme scientifique est disponible sur le site local de l'école :
https://natural-sciences.nwu.ac.za/paa/3MC-CIMPA-School-2025
Langue officielle de l'école : anglais