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网络中社会传播的传染病建模。

Infectious disease modeling of social contagion in networks.

机构信息

Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts, United States of America.

出版信息

PLoS Comput Biol. 2010 Nov 4;6(11):e1000968. doi: 10.1371/journal.pcbi.1000968.

Abstract

Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for 'automatic' (or 'spontaneous') non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

摘要

许多行为现象已经被发现通过社交网络在人际间传播,其方式类似于传染病。然而,社会传播与传统传染病之间的一个重要区别是,行为现象不仅可以通过社会传播获得,也可以通过非社会机制获得。我们通过改编经典疾病模型来包括“自动”(或“自发”)非社会感染的可能性,引入了一种研究这些现象的新理论框架(SISa 模型)。我们通过检查弗雷明汉心脏研究网络中肥胖的传播来提供使用该框架的示例。使用纵向网络传输数据验证模型的交互假设。我们发现目前肥胖的增长率为每年 2%,每增加一个肥胖的社交接触,增长率增加 0.5 个百分点。从肥胖中恢复的速度为每年 4%,且与非肥胖接触的数量无关。该模型预测长期肥胖患病率约为 42%,并可用于评估不同干预措施对稳定状态肥胖的影响。模型预测定量再现了肥胖实际的历史时间过程。我们发现,自 20 世纪 70 年代以来,肥胖的恢复率相对稳定,而自发感染和传播的速度都随着时间的推移而稳步增加。这表明肥胖流行可能是由于自发和传染性肥胖率的增加所驱动,而不是由于减肥率的降低。SISa 模型的一个关键特征是其通过定量比较自发感染与传染性感染的相对重要性来描述社会传播相对重要性的能力。它为研究任何可能自发出现的状态(如情绪、行为、健康状况、想法或带有储存库的疾病)在人际间的传播提供了一个理论框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bd6/2973808/37645bd84e67/pcbi.1000968.g001.jpg

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