Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread.
Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread.
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DOI：
10.1371/journal.pcbi.1002673
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发表时间：
2012
影响因子：
4.3
通讯作者：
Merler S
中科院分区：
文献类型：
--
作者：
Fumanelli L;Ajelli M;Manfredi P;Vespignani A;Merler S
个体之间的社会接触模式编码了传染病的传播途径，是流行病现实表征和建模的关键因素。不幸的是，即使在年龄组之间的混合模式的粗略水平上，收集关于人群接触模式的高质量实验数据也是一项非常困难的任务。在这里，我们提出了一种替代路线的混合模式，依赖于建设的虚拟人口参数化与非常详细的人口普查和人口统计数据的估计。我们提出了26个欧洲国家的人口建模和人口年龄组之间相应的合成接触矩阵的生成。该方法进行了验证，在六个欧洲国家的混合模式的最广泛的调查研究获得的矩阵进行了详细的比较。这里介绍的方法允许在欧洲的混合模式进行大规模的比较，突出一般的共同特点，以及国家的具体差异。我们发现流行病学相关的数量（繁殖数量和攻击率）和人口的社会人口特征，如人口的平均年龄和小学周期的持续时间之间存在明确的关系。这项研究提供了一种数值方法，用于生成人类混合模式，可用于提高数学模型的准确性，在没有具体的实验数据。由人传人病原体引起的传染病的动态在很大程度上取决于人与人之间的接触模式。关于接触模式的高质量观察数据通常以特定年龄接触矩阵的形式呈现，很难收集，目前仅在全世界少数国家可用。在这里，我们提出了一种计算方法，基于代理的虚拟社会的模拟，允许估计的接触模式的年龄为26个欧洲国家。我们验证了估计接触矩阵对接触模式的最广泛的实地研究，在8个欧洲国家收集的数据。我们发现，我们的接触矩阵有一些共同的特点，例如，个人倾向于优先与个人自己的年龄，和国家特定的差异，这可以部分解释为人口结构的差异，由于不同的人口轨迹后，二战。我们的分析突出了流行病学参数和人群的社会人口特征之间的明确相关性。这项研究提供了接触矩阵的许多欧洲国家的具体实验数据仍然不可用的第一个估计。
Social contact patterns among individuals encode the transmission route of infectious diseases and are a key ingredient in the realistic characterization and modeling of epidemics. Unfortunately, the gathering of high quality experimental data on contact patterns in human populations is a very difficult task even at the coarse level of mixing patterns among age groups. Here we propose an alternative route to the estimation of mixing patterns that relies on the construction of virtual populations parametrized with highly detailed census and demographic data. We present the modeling of the population of 26 European countries and the generation of the corresponding synthetic contact matrices among the population age groups. The method is validated by a detailed comparison with the matrices obtained in six European countries by the most extensive survey study on mixing patterns. The methodology presented here allows a large scale comparison of mixing patterns in Europe, highlighting general common features as well as country-specific differences. We find clear relations between epidemiologically relevant quantities (reproduction number and attack rate) and socio-demographic characteristics of the populations, such as the average age of the population and the duration of primary school cycle. This study provides a numerical approach for the generation of human mixing patterns that can be used to improve the accuracy of mathematical models in the absence of specific experimental data. The dynamics of infectious diseases caused by pathogens transmissible from human to human strongly depends on contact patterns between individuals. High quality observational data on contact patterns, usually presented in the form of age-specific contact matrices, are difficult to gather and are currently available only for few countries worldwide. Here we propose a computational approach, based on the simulation of a virtual society of agents, allowing the estimation of contact patterns by age for 26 European countries. We validate the estimated contact matrices against those obtained by the most extensive field study on contact patterns, with data collected in eight European countries. We show that our contact matrices share some common features, e.g. individuals tend to mix preferentially with individuals their own age, and country-specific differences, which can be partly explained by differences in population structures due to different demographic trajectories followed after WWII. Our analysis highlights well defined correlations between epidemiological parameters and socio-demographic features of the populations. This study provides the first estimates of contact matrices for many European countries where specific experimental data are still not available.
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DOI：
10.1056/nejmoa0905498
发表时间：
2009-12-31
期刊：
The New England journal of medicine
影响因子：
--
作者：
Cauchemez S;Donnelly CA;Reed C;Ghani AC;Fraser C;Kent CK;Finelli L;Ferguson NM
通讯作者：
Ferguson NM
DOI：
10.1073/pnas.0906910106
发表时间：
2009-12-22
影响因子：
11.1
作者：
Balcan, Duygu;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
2
作者：
Guzzetta, Giorgio;Ajelli, Marco;Yang, Zhenhua;Merler, Stefano;Furlanello, Cesare;Kirschner, Denise
通讯作者：
Kirschner, Denise
影响因子：
4.3
作者：
Eames KT;Tilston NL;Brooks-Pollock E;Edmunds WJ
通讯作者：
Edmunds WJ
DOI：
10.1098/rspb.1997.0131
发表时间：
1997-07-22
影响因子：
4.7
作者：
Edmunds, WJ;OCallaghan, CJ;Nokes, DJ
通讯作者：
Nokes, DJ