Department of Computer Science, University of Saskatchewan, Saskatoon, Canada.
BMC Med Inform Decis Mak. 2012 May 2;12:35. doi: 10.1186/1472-6947-12-35.
The contact networks between individuals can have a profound impact on the evolution of an infectious outbreak within a network. The impact of the interaction between contact network and disease dynamics on infection spread has been investigated using both synthetic and empirically gathered micro-contact data, establishing the utility of micro-contact data for epidemiological insight. However, the infection models tied to empirical contact data were highly stylized and were not calibrated or compared against temporally coincident infection rates, or omitted critical non-network based risk factors such as age or vaccination status.
In this paper we present an agent-based simulation model firmly grounded in disease dynamics, incorporating a detailed characterization of the natural history of infection, and 13 weeks worth of micro-contact and participant health and risk factor information gathered during the 2009 H1N1 flu pandemic.
We demonstrate that the micro-contact data-based model yields results consistent with the case counts observed in the study population, derive novel metrics based on the logarithm of the time degree for evaluating individual risk based on contact dynamic properties, and present preliminary findings pertaining to the impact of internal network structures on the spread of disease at an individual level.
Through the analysis of detailed output of Monte Carlo ensembles of agent based simulations we were able to recreate many possible scenarios of infection transmission using an empirically grounded dynamic contact network, providing a validated and grounded simulation framework and methodology. We confirmed recent findings on the importance of contact dynamics, and extended the analysis to new measures of the relative risk of different contact dynamics. Because exponentially more time spent with others correlates to a linear increase in infection probability, we conclude that network dynamics have an important, but not dominant impact on infection transmission for H1N1 transmission in our study population.
个体之间的接触网络对网络内传染病爆发的演变有深远的影响。使用合成和经验收集的微观接触数据，研究了接触网络与疾病动力学之间的相互作用对感染传播的影响，从而证实了微观接触数据在流行病学研究中的实用性。然而，与经验接触数据相关的感染模型高度理想化，没有针对时间上一致的感染率进行校准或比较，也没有纳入关键的非网络风险因素，如年龄或疫苗接种状况。
在本文中，我们提出了一个基于代理的模拟模型，该模型牢牢扎根于疾病动力学，对感染的自然史进行了详细的描述，并整合了在 2009 年 H1N1 流感大流行期间收集的 13 周微观接触和参与者健康及风险因素信息。
我们证明了基于微观接触数据的模型与研究人群中观察到的病例数一致，基于时间度的对数衍生出了新的指标，用于根据接触动态特性评估个体风险，并提出了关于内部网络结构对个体层面疾病传播影响的初步发现。
通过对基于代理的蒙特卡罗模拟集合的详细输出进行分析，我们能够使用经验基础的动态接触网络再现许多可能的感染传播场景，提供了一个经过验证和有根据的模拟框架和方法。我们证实了最近关于接触动力学重要性的发现，并将分析扩展到了不同接触动力学相对风险的新指标。由于与他人的接触时间呈指数级增长与感染概率呈线性相关，我们得出结论，对于我们研究人群中的 H1N1 传播，网络动力学对感染传播有重要影响，但不是主导因素。
