To model a comprehensive general model, the multi-level sub-systems links of obesity should be explored, which can become so unwieldy and complex that results are no longer transparent, and validation becomes nearly impossible. The multi-level sub-systems interconnections of obesity drivers must be investigated to derive general model, potentially able to clarify complex and indirect interconnections Modeling multi-level model can become confusing and complex that results are no longer transparent, making validation impossible. A holistic model explaining the indirect drivers of obesity in complex food system is missing. Most models are designed with a specific question and focus on the limited number of links specific to a region or county. Multiple authors used different system dynamic (SD) techniques and methods to model, predict, classify, and explain the prevalence of obesity and driver’s interconnection. A complex system model that can explain inter-connections and correlations of drivers and can examine non-linear dynamics, time-delay effects, multiple interactions, and feedback is required. Due to obesity’s global scope, heterogeneous drivers interacting in non-linear ways, and the lack of a single solution for variation in outcomes, a complex system is needed ( 3). Obesity is a multidimensional, systemic issue that affects a variety of domains, including social interactions, infrastructure, environment, and biology ( 2). There are multiple causes and effects that contribute to obesity. Obesity is a global public health and economic issue that harms people’s physical and mental health, reduces their quality of life and life expectancy, and significantly increases the cost of healthcare systems ( 1). Over the past two decades, there has been a significant rise in the prevalence of obesity, which has gradually turned into a global epidemic. This paper reviews existing computational models and datasets used to compute obesity outcomes to design a conceptual framework for establishing a macro-level generalized obesity model. The model should consider all interconnected multi-system drivers to address obesity prevalence and intervention. Most models are designed to reflect through time and space at the individual level in a population, which indicates the need for a macro-level generalized population model. Reviewed literature shows a growing adaptation of the machine-learning model in recent years dealing with mechanisms and interventions in social influence, nutritional diet, eating behavior, physical activity, built environment, obesity prevalence prediction, distribution, and healthcare cost-related outcomes of obesity. Obesity-related data derived from multiple complex systems spanning media, social, economic, food activity, health records, and infrastructure (sensors, smartphones, etc.) can assist us in understanding the relationship between obesity drivers for more efficient prevention and treatment. 3Plan-Based Robot Control German Research Center for Artificial Intelligence, Osnabrück, Germany.2Knowledge-Based Systems Research Group, Institute of Computer Science, University of Osnabrück, Osnabrück, Germany.1Food Data Group, German Institute of Food Technologies (DIL e.V.), Quakenbrück, Germany.Anita Bhatia 1,2* Sergiy Smetana 1 Volker Heinz 1 Joachim Hertzberg 2,3
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