Identifying the consequences of obesity on chronic diseases through modeling, analysis, and simulation

Abstract

This thesis entitled "Identifying the consequences of obesity on chronic diseases through modeling, analysis, and simulation" is a study that embarks on a comprehensive exploration of the intricate relationship between obesity and chronic illnesses, employing modeling, analysis, and simulation techniques to elucidate the consequential impacts. Obesity is a worldwide problem currently at its peak or increasing rapidly. Globally, it is becoming a significant health concern. It results from the modern lifestyle, characterized by downsized physical activity and enriched energy intake. It is associated with various diseases and conditions, especially cardiovascular diseases, type 2 diabetes, Alzheimer's, obstructive sleep apnea, certain types of cancer, and osteoarthritis. As a result, it has been found to reduce life expectancy. Unusual rapid rates of chronic diseases and obesity are spreading throughout populations.

One of the ways to tackle or understand the long-term disruption due to obesity in the human body without invasive techniques that are cost-effective is utilizing epidemiological models. The scientific study of the variables affecting the frequency and distribution of illness, injuries, and other health-related events, as well as their causes and effects on populations of humans and animals, is known as epidemiology. Furthermore, studies have been done on creating patterns in spatial epidemic models, a subfield of epidemiology called ``spatial epidemiology." It studies health outcomes patterns, spatial distribution, and the factors influencing them within a community. It investigates diseases' spatial distribution and how different environmental, socioeconomic, and demographic factors affect these trends. By examining the spatial patterns, disease clusters can be quickly detected, the variables influencing their distribution can be comprehended, and disease control and prevention plans can be created. Spatial epidemiology with self-diffusion has emerged as a key research field for understanding the origins and effects of geographical variability in chronic diseases.

This thesis aims to predict the dynamics of chronic diseases due to obesity. To achieve this, spatial and non-spatial models are formed according to the biological problem under consideration. We performed extensive analytical and numerical analysis to understand obesity-related disease dynamics and potential outcomes.