School of Mathematics, Cardiff University, Cardiff CF24 4AG, UK.
Aneurin Bevan Continuous Improvement, Aneurin Bevan University Health Board, Caerleon NP18 3XQ, UK.
Int J Environ Res Public Health. 2023 Jul 26;20(15):6451. doi: 10.3390/ijerph20156451.
Previous research has highlighted the significant role social networks play in the spread of non-communicable chronic diseases. In our research, we seek to explore the impact of these networks in more detail and gain insight into the mechanisms that drive this. We use obesity as a case study. To achieve this, we develop a generalisable hybrid simulation and optimisation approach aimed at gaining qualitative and quantitative insights into the effect of social networks on the spread of obesity. Our simulation model has two components. Firstly, an agent-based component mimics the dynamic structure of the social network within which individuals are situated. Secondly, a system dynamics component replicates the relevant behaviours of those individuals. The parameters from the combined model are refined and optimised using longitudinal data from the United Kingdom. The simulation produces projections of Body Mass Index broken down by different age groups and gender over a 10-year period. These projections are used to explore a range of scenarios in a computational study designed to address our research aims. The study reveals that, for the youngest population sub-groups, the network acts to magnify the impact of external and social factors on changes in obesity, whereas, for older sub-groups, the network mitigates the impact of these factors. The magnitude of that impact is inversely correlated with age. Our approach can be used by public health decision makers as well as managers in adult weight management services to enhance initiatives and strategies intended to reduce obesity. Our approach is generalisable to understand the impact of social networks on similar non-communicable diseases.
先前的研究强调了社交网络在非传染性慢性病传播中的重要作用。在我们的研究中，我们试图更详细地探讨这些网络的影响，并深入了解推动这一现象的机制。我们选择肥胖作为案例研究。为此，我们开发了一种可推广的混合模拟和优化方法，旨在深入了解社交网络对肥胖传播的影响。我们的模拟模型有两个组成部分。首先，基于代理的组件模拟了个体所处的社交网络的动态结构。其次，系统动力学组件复制了这些个体的相关行为。通过使用来自英国的纵向数据，对组合模型的参数进行了细化和优化。该模拟生成了 10 年内按不同年龄组和性别细分的身体质量指数（BMI）预测。这些预测被用于在一项旨在解决我们研究目标的计算研究中探索一系列情景。研究表明，对于最年轻的人群亚组，网络放大了外部和社会因素对肥胖变化的影响，而对于年龄较大的亚组，网络则减轻了这些因素的影响。这种影响的程度与年龄成反比。我们的方法可以为公共卫生决策者以及成人体重管理服务的管理人员提供帮助，以增强旨在减少肥胖的计划和策略。我们的方法可以推广到理解社交网络对类似非传染性疾病的影响。
