Applications of small-world network theory in alcohol epidemiology
Applications of small-world network theory in alcohol epidemiology
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DOI：
10.15288/jsa.2006.67.591
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发表时间：
2006-07-01
期刊：
影响因子：
--
通讯作者：
Gleeson, James P.
中科院分区：
文献类型：
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作者：
Braun, Richard J.;Wilson, Robert A.;Gleeson, James P.
目的：本研究建立了结构性人群中酒精滥用的数学模型，如社区和大学校园。这项研究采用了一种网络模型，该模型除了个人熟人之外，还能够纳入各种形式的连接成员身份，例如地理上的接近和共同的组织。该模型还纳入了一个弹性维度，表明网络中每个人对酒精滥用的易感性。该模型有能力模拟酒精滥用者进入非滥用者网络的影响，无论是治疗的结果还是自助组织的成员。方法：本研究采用小世界模型。每个人的三次方程(图上的顶点)控制着一个人在酒精依赖方面在0和I之间的状态的演变，1表示酒精依赖的绝对确定性。模拟依赖于初始条件、网络的结构和网络的弹性分布。这些模拟结合了社交网络的多种实现，显示了不同网络结构的影响。结果：该模型表明，通过治疗相对较小比例的研究人群，可以将酒精滥用的流行率降至最低。在我们研究的小群体中，临界点是研究群体的10%或更少，但我们强调，这是在该模型的限制和假设范围内的。结论：使用一个简单的模型，结合结构化人群中社会网络邻居的影响，显示出有助于为治疗和预防政策提供信息的前景。
Objective: This study develops a mathematical model of alcohol abuse in structured populations, such as communities and college campuses. The study employs a network model that has the capacity to incorporate a variety of forms of connectivity membership besides personal acquaintance, such as geographic proximity and common organizations. The model also incorporates a resilience dimension that indicates the susceptibility of each individual in a network to alcohol abuse. The model has the capacity to simulate the effect of moving alcohol abusers into networks of nonabusers, either as the result of treatment or membership in self-help organizations. Method: The study employs a small-world model. A cubic equation for each person (vertex on a graph) governs the evolution of an individual's state between 0 and I with regard to alcohol dependence, with 1 indicating absolute certainty of alcohol dependence. The simulations are dependent on initial conditions, the structure of the network, and the resilience distribution of the network. The simulations incorporate multiple realizations of social networks, showing the effect of different network structures. Results: The model suggests that the prevalence of alcohol abuse can be minimized by treating a relatively small percentage of the study population. In the small populations that we studied, the critical point was 10% or less of the study population, but we emphasize that this is within the limitations and assumptions of this model. Conclusions: The use of a simple model that incorporates the influence of the social network neighbors in structured populations shows promise for helping to inform treatment and prevention policy.