Mathematical model for cancer prevalence and cancer mortality

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The first part of the paper designs a deterministic model to describe cancer prevalence and mortality in a population. Next the asymptotic properties of the model are investigated. In the second part, the model is applied to real-world data. For selected model data, a numerical solution is found to the differential equations describing the model, a long-term prediction is made with its results compared with those of predictions made by regression analysis, which are often used to model the prevalence and mortality in the present literature. It is shown that, although for short-term predictions (up to 10 years) both approaches are nearly equivalent, there is a major difference between them if a longer-term prediction is made and finding a reliable prediction for a period longer than 10 years based on short time series seems to be unlikely.