Nonlinear projection methods for prediction of trends in cancer incidence and mortality

Main aim of this project is to develop a prediction model for cancer projection, with detailed information regarding incidence, mortality and other measures of cancer burden for the most common types of cancer, presented by age, sex, time and geographic locations. These data can help stimulate new research as well as assist decision-making and priority-setting at the individual, community, provincial/territorial and national levels. This prediction model can be implemented by using historical data to model trends of cancer risk, and extrapolating the trends into the future to project the rates and numbers. Monitoring cancer incidence and mortality time trends is essential for cancer research and health-care planning. Predicting the future cancer burden is one of the first steps in knowing how to allocate resources most effectively and formulate guidelines on cancer screening to reduce the morbidity and mortality from cancer, and to improve the quality of life of cancer patients and their families.

Faculty Supervisor:

Dr. William Melek


Smita Kachroo


Alpha Global IT




Life sciences


University of Waterloo



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