Statistical and Mathematical Modelling of Health Issues

Dear Colleagues,

Health issues are an enormous problem worldwide, with scientists seeking solutions to the various problems in health sciences, including infectious diseases, cancer, heart diseases, etc. Mathematical and statistical modeling are among the best approaches in the diagnosis or treatment of these kinds of diseases. Preventing the spread or occurrence of diseases via control structures is one of the leading aims of mathematical/statistical modelling in health sciences. Because the constructed models are proven with the necessary methods, the results of these models can be applied to real life without any concerns. The results of different kinds of models (ODE, PDE, FDE, SDE, etc.) in health sciences provide doctors and patients with new perspectives and ideas for the control, diagnosis, and treatment of diseases. Thus, introducing new parameters or variables for the models enables further and, more importantly, proven results about diseases. The kind of differential equations (ODE, PDE, FDE, SDE, etc.) may differ and depends on the type or structure of the disease and field of mathematicians. Additionally, changes in global conditions lead to changes in current and the emergence of new health problems. Because of this, and because of the huge range of the field, many questions are still open. Hence, modeling approaches should be constructed and developed for accurate solutions. The aim of this issue is to comprehensively discuss the topic of modeling approaches to the health sciences. We welcome studies on many kinds of methods, algorithms, and tools to infer health issues. We welcome original research in these areas.

Topics:
(1) Modeling with ODE in health issues
(2) Modeling with FDE in health issues
(3) Modeling with SDE in health issues
(4) Modeling with PDE in health issues
(5) Modeling with statistical distributions in health issues
(6) Optimal control theory in health issues

Prof. Dr. Evren Hınçal
Guest Editor

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• cancer
• ordinary differential equations
• infectious diseases
• distributions in statistics
• optimal control